Cooperative Information Agents X: 10th International Workshop, CIA 2006, Edinburgh, UK, September 11-13, 2006, Proceedings

Lecture Notes in Artificial Intelligence Edited by J. G. Carbonell and J. Siekmann Subseries of Lecture Notes in Comput...

Author: Matthias Klusch | Michael Rovatsos | Terry R. Payne

17 downloads 1090 Views 9MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!

Report copyright / DMCA form

DOWNLOAD PDF

Lecture Notes in Artificial Intelligence Edited by J. G. Carbonell and J. Siekmann

Subseries of Lecture Notes in Computer Science

4149

Matthias Klusch Michael Rovatsos Terry R. Payne (Eds.)

Cooperative Information Agents X 10th International Workshop, CIA 2006 Edinburgh, UK, September 11-13, 2006 Proceedings

13

Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Matthias Klusch German Research Center for Artificial Intelligence Stuhlsatzenhausweg 3, 66123 Saarbrücken, Germany E-mail: [email protected] Michael Rovatsos The University of Edingburgh School of Informatics Appleton Tower 3.12, Edinburgh, UK E-mail: [email protected] Terry R. Payne University of Southampton Southampton, SO17 1BJ, UK E-mail: [email protected]

Library of Congress Control Number: 2006931573

CR Subject Classification (1998): I.2.11, I.2, H.4, H.3.3, H.2, C.2.4, H.5 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13

0302-9743 3-540-38569-X Springer Berlin Heidelberg New York 978-3-540-38569-1 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2006 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 11839354 06/3142 543210

Preface

These are the proceedings of the Tenth International Workshop on Cooperative Information Agents (CIA 2006), held at the National eScience Centre, Edinburgh, UK, September 11–13, 2006. This year, the annual meeting of the IEEE Computer Society Standards Organisation Committee FIPA on Intelligent and Physical Agents was co-located with CIA 2006. In today’s networked world of linked, heterogeneous, pervasive computer systems, devices, and information landscapes, intelligent coordination and provision of relevant added-value information at any time, anywhere, by means of cooperative information agents becomes increasingly important for a variety of applications. An information agent is a computational software entity that has access to one or multiple heterogeneous and geographically dispersed data and information sources. It pro-actively searches for and maintains information on behalf of its human users or other agents preferably just in time. In other words, it manages and overcomes the diﬃculties associated with information overload in open, pervasive information and service landscapes. Cooperative information agents may collaborate with each other to accomplish both individual and shared joint goals depending on the actual preferences of their users, budgetary constraints, and resources available. One major challenge of developing agent-based intelligent information systems in open environments is to balance the autonomy of networked data, information, and knowledge sources with the potential payoﬀ of leveraging them by the use of information agents. The objective of the international workshop series on Cooperative Information Agents (CIA), since its establishment in 1997, has been to provide a small but very distinguished, interdisciplinary forum for researchers and practitioners to get informed about, present, and discuss the state of the art in research and development of agent-based, intelligent, and cooperative information systems and applications for the Internet and Web. Each event in the series oﬀers regular and invited talks of excellence, given by renowned experts in the ﬁeld, as well as a selected set of system demonstrations, and honors innovative research and development of information agents by means of a best paper award and a system innovation award, respectively. The proceedings of the series are regularly published as volumes of Springer’s Lecture Notes in Artiﬁcial Intelligence (LNAI) series. In keeping with its tradition, this year’s workshop featured a sequence of regular and invited talks of excellence given by leading researchers covering a broad area of topics of interest, such as agent-based information provision, agents and services, rational cooperation, resource and task allocation, communication and cooperation, agent-based grid computing, and applications. This year’s special topic of interest was agent-based semantic grid computing systems and their applications.

VI

Preface

CIA 2006 featured 4 invited and 29 regular papers selected from 58 submissions. The result of the peer review of all contributions is included in this volume: a rich collection of papers describing interesting, inspiring, and advanced work on research and development of intelligent information agents worldwide. The proceedings of previous CIA workshops have been published by Springer as Lecture Notes in Artiﬁcial Intelligence volumes: 1202 (1997), 1435 (1998), 1652 (1999), 1860 (2000), 2182 (2001), 2446 (2002), 2782 (2003), 3191 (2004), 3550 (2005). This year the CIA System Innovation Award and the CIA Best Paper Award were sponsored by Whitestein Technologies AG, Switzerland, and the CIA workshop series, respectively. There has been limited ﬁnancial support available to a limited number of students as (co-)authors of accepted papers to enable them to present their work at the CIA 2006 workshop; these grants were sponsored by the IEEE FIPA standards committee and the British Society for the Study of Artiﬁcial Intelligence and Simulation of Behaviour (AISB). The CIA 2006 workshop was organized in cooperation with the Association for Computing Machinery (ACM) and the Global Grid Forum (GGF). In addition, we are very much indebted to our sponsors, whose ﬁnancial support made this event possible. The sponsors of CIA 2006 were: National eScience Centre, UK Centre for Intelligent Systems and their Applications, The University of Edinburgh, UK Whitestein Technologies, Switzerland British Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB) IEEE Computer Society Standards Organisation Committee FIPA on Intelligent and Physical Agents We are particularly grateful to the authors and invited speakers for contributing their latest and inspiring work to this workshop, as well as to the members of the program committee and the additional reviewers for their critical reviews of submissions. Finally, a deep thanks goes to each of the committed members of the local organization team from the University of Edinburgh and the eScience Institute for their hard work in providing the CIA 2006 event, which marked the tenth anniversary of the series, with a traditionally comfortable, modern, and all-inclusive location and a very nice social program. We hope you enjoyed CIA 2006 and were inspired for your own work!

September 2006

Matthias Klusch, Michael Rovatsos, Terry Payne

Organization

General Chair Matthias Klusch

DFKI, Germany

Program Co-chairs Michael Rovatsos Terry Payne

The University of Edinburgh, UK University of Southampton, UK

Program Committee Karl Aberer Wolfgang Benn Federico Bergenti Bernard Burg Monique Calisti Cristiano Castelfranchi John Debenham Yves Demazeau Boi Faltings Fausto Giunchiglia Marie-Pierre Gleizes Rune Gustavsson Heikki Helin Brian Henderson-Sellers Michael Huhns Toru Ishida Catholijn Jonker Hillol Kargupta Ewan Klein Christoph Koch Manolis Koubarakis Sarit Kraus Daniel Kudenko Maurizio Lenzerini Victor Lesser Jiming Liu Stefano Lodi Aris Ouksel Sascha Ossowski Paolo Petta

EPF Lausanne, Switzerland TU Chemnitz, Germany U Parma, Italy Panasonic Research, USA Whitestein Technologies, Switzerland U Siena, Italy TU Sydney, Australia LEIBNIZ/IMAG, France EPF Lausanne, Switzerland IRST-ITC, Italy IRIT Toulouse, France TH Blekinge, Sweden TeliaSonera, Finland/Sweden TU Sydney, Australia U South Carolina, USA Kyoto University, Japan U Nijmegen, The Netherlands UMBC Baltimore, USA U Edinburgh, UK U Saarland, Germany TU Crete, Greece Bar-Ilan U, Israel U York, UK U Rome, Italy U Massachusetts, USA Hong Kong Baptist U, China U Bologna, Italy U Illinois, USA U Rey Juan Carlos Madrid, Spain Medical U Vienna, Austria

VIII

Organization

Alun Preece Omer Rana Jeﬀrey Rosenschein Marie-Christine Rousset Ken Satoh Onn Shehory Carles Sierra Steﬀen Staab Hiroki Suguri Katia Sycara Rainer Unland Gottfried Vossen

U Aberdeen, UK U Cardiﬀ, UK Hebrew U, Israel U Paris-Sud, France National Institute for Informatics, Japan IBM Research, Israel CSIC Barcelona, Spain U Koblenz, Germany Comtech Sendai, Japan Carnegie Mellon U, USA U Duisburg-Essen, Germany U Muenster, Germany

Additional Reviewers Adam Barker Alexandra Berger Val´erie Camps Daniel Dahl Vasilios Darlagiannis Esther David Naoki Fukuta Dorian Gaertner Roberto Ghizzioli Andrea Giovannucci Pierre Glize Dominic Greenwood Koen Hindriks Marc-Philippe Huget Tobias John Sindhu Joseph Ron Katz Fabius Klemm Dimitri Melaye Eric Platon Annett Priemel Giovanni Rimassa Alex Rogers Roman Schmidt Joachim Schwieren Frank Seifert Sebastian Stein Eiji Tokunaga Dmytro Tykhonov

Table of Contents

Invited Contributions Semantic Web Research Anno 2006: Main Streams, Popular Fallacies, Current Status and Future Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Frank van Harmelen A Research Agenda for Agent-Based Service-Oriented Architectures . . . . . Michael N. Huhns The Helpful Environment: Distributed Agents and Services Which Cooperate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Austin Tate Voting in Cooperative Information Agent Scenarios: Use and Abuse . . . . . Jeﬀrey S. Rosenschein, Ariel D. Procaccia

1

8

23

33

Agent Based Information Provision Agents for Information-Rich Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . John Debenham, Simeon Simoﬀ Information Agents for Optimal Repurposing and Personalization of Web Contents in Semantics-Aware Ubiquitous and Mobile Computing Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fernando Alonso, Sonia Frutos, Miguel Jim´enez, Javier Soriano Turn Taking for Artiﬁcial Conversational Agents . . . . . . . . . . . . . . . . . . . . . . Fredrik Kronlid Inducing Perspective Sharing Between a User and an Embodied Agent by a Thought Balloon as an Input Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Satoshi V. Suzuki, Hideaki Takeda

51

66

81

96

Applications Agent-Based Analysis and Support for Incident Management . . . . . . . . . . . 109 Mark Hoogendoorn, Catholijn M. Jonker, Jan Treur, Marian Verhaegh

X

Table of Contents

A Distributed Agent Implementation of Multiple Species Flocking Model for Document Partitioning Clustering . . . . . . . . . . . . . . . . . . . . . . . . . 124 Xiaohui Cui, Thomas E. Potok Coverage Density as a Dominant Property of Large-Scale Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Osher Yadgar, Sarit Kraus

Agents and Services Selecting Web Services Statistically . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 David Lambert, David Robertson Conversation-Based Speciﬁcation and Composition of Agent Services . . . . 168 Quoc Bao Vo, Lin Padgham Evaluating Dynamic Services in Bioinformatics . . . . . . . . . . . . . . . . . . . . . . . 183 Ma´ıra R. Rodrigues, Michael Luck

Learning A Classiﬁcation Framework of Adaptation in Multi-Agent Systems . . . . . . 198 C´esar A. Mar´ın, Nikolay Mehandjiev Market-Inspired Approach to Collaborative Learning . . . . . . . . . . . . . . . . . . 213 Jan Toˇziˇcka, Michal Jakob, Michal Pˇechouˇcek Improving Example Selection for Agents Teaching Ontology Concepts . . . 228 Mohsen Afsharchi, Behrouz H. Far

Resource and Task Allocation Egalitarian Allocations of Indivisible Resources: Theory and Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Paul-Amaury Matt, Francesca Toni Iterative Query-Based Approach to Eﬃcient Task Decomposition and Resource Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 Michal Pˇechouˇcek, Ondˇrej Lerch, Jiˇr´ı B´ıba Multilevel Approach to Agent-Based Task Allocation in Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Martin Reh´ ak, Pˇremysl Volf, Michal Pˇechouˇcek

Table of Contents

XI

Rational Cooperation (1) Learning to Negotiate Optimally in Non-stationary Environments . . . . . . . 288 Vidya Narayanan, Nicholas R. Jennings Eliminating Interdependencies Between Issues for Multi-issue Negotiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Koen Hindriks, Catholijn M. Jonker, Dmytro Tykhonov The Distortion of Cardinal Preferences in Voting . . . . . . . . . . . . . . . . . . . . . . 317 Ariel D. Procaccia, Jeﬀrey S. Rosenschein

Rational Cooperation (2) Risk-Bounded Formation of Fuzzy Coalitions Among Service Agents . . . . . 332 Bastian Blankenburg, Minghua He, Matthias Klusch, Nicholas R. Jennings A Simple Argumentation Based Contract Enforcement Mechanism . . . . . . 347 Nir Oren, Alun Preece, Timothy J. Norman A Fuzzy Approach to Reasoning with Trust, Distrust and Insuﬃcient Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 Nathan Griﬃths

Communication and Cooperation Performative Patterns for Designing Veriﬁable ACLs . . . . . . . . . . . . . . . . . . 375 Nicola Dragoni, Mauro Gaspari Enabling Mobile Agents Interoperability Through FIPA Standards . . . . . . 388 Joan Ametller-Esquerra, Jordi Cucurull-Juan, Ramon Mart´ı, Guillermo Navarro, Sergi Robles Characterising Agents’ Behaviours: Selecting Goal Strategies Based on Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 Jos´e Cascalho, Luis Antunes, Milton Corrˆea, Helder Coelho A Framework of Cooperative Agents with Implicit Support for Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 Riza Cenk Erdur, Inan¸c Seylan Specifying Protocols for Knowledge Transfer and Action Restriction in Multiagent Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Mar´ıa Adela Grando, Christopher David Walton

XII

Table of Contents

Agent Based Grid Computing Flexible Service Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446 Adam Barker, Robert G. Mann Using Electronic Institutions to Secure Grid Environments . . . . . . . . . . . . . 461 Ronald Ashri, Terry R. Payne, Michael Luck, Mike Surridge, Carles Sierra, Juan Antonio Rodriguez Aguilar, Pablo Noriega Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477

Semantic Web Research Anno 2006: Main Streams, Popular Fallacies, Current Status and Future Challenges Frank van Harmelen Vrije Universiteit Amsterdam [email protected]

Abstract. In this topical1 paper we try to give an analysis and overview of the current state of Semantic Web research. We point to diﬀerent interpretations of the Semantic Web as the reason underlying many controversies, we list (and debunk) four false objections which are often raised against the Semantic Web eﬀort. We discuss the current status of the Semantic Web work by reviewing the current answers to four central research questions that need to be answered, and by surveying the uptake of Semantic Web technology in diﬀerent application areas. Finally, we try to identify the main challenges facing the Semantic Web community.

1

Which Semantic Web?

It has already been pointed out by Marshall and Shipman in [1] that the term “Semantic Web” is used to describe a variety of diﬀerent goals and methods. They distinguish (1) the Semantic Web as a universal library for human access; (2) as the habitat for automated agents and web-services 2 ; and (3) as a method for federating a variety of databases and knowledge bases. And although we in no way share their rather pessimistic analysis of the possibilities for each of these three scenario’s (founded as they are on rather strawman versions of each of them), we do agree that it is important to unravel the diﬀerent ambitions that underly the “Semantic Web” term. In the current Semantic Web work, we distinguish two main goals. These goals are often unspoken, but the diﬀerences between them often account for many debates on design choices, on the applicability of various techniques, and on the feasibility of applications. 1

2

In the sense of: “of current interest”, “concerning contemporary topics of limited validity”. Although Marshall and Shipman do not actually use the term web-services.

M. Klusch, M. Rovatsos, and T. Payne (Eds.): CIA 2006, LNAI 4149, pp. 1–7, 2006. c Springer-Verlag Berlin Heidelberg 2006

2

F. van Harmelen

Interpretation 1: The Semantic Web as the Web of Data In the ﬁrst interpretation (close to Marshall and Shipman’s third option), the main aim of the Semantic Web is to enable the integration of structured and semi-structured data-sources over the Web. The main recipe is to expose datasets on the web in RDF format, to use RDF Schema to express the intended semantics of these data-sets, in order to enable the integration and unexpected re-use of these data-sets. A typical use-cases for this version of the Semantic Web is the combination of geo-data with a set of consumer ratings for restaurants in order to provide an enriched information source. Interpretation 2: The Semantic Web as an Enrichment of the Current Web In the second interpretation, the aim of the Semantic Web is to improve the current World Wide Web. Typical use-cases here are improved search engines, dynamic personalisation of web-sites, and semantic enrichment of existing web-pages. The source of the required semantic meta-data in this version of the Semantic Web is mostly claimed to come from automatic sources: concept-extraction, named-entity recognition, automatic classiﬁcation, etc. More recently, the insight is gaining ground that the required semantic markup can also be produced by social mechanisms of in communities that provide large-scale human-produced markup. Of course there are overlaps between these two versions of the Semantic Web: they both rely on the use of semantic markup, typically in the form of metadata described by ontology-like schemata. But perhaps more noticeable are the signiﬁcant diﬀerences: diﬀerent goals, diﬀerent sources of semantics, diﬀerent use-cases, diﬀerent technologies.

2

Four Popular Fallacies

The Semantic Web is subject to a stream of strongly and often polemically voiced criticisms3 . Unfortunately, not all of these are equally well informed. A closer analysis reveals that many of these polemics attribute a number of false assumptions or claims to the Semantic Web programme. In this section we aim to identify and debunk these fallacies. Fallacy 1: The Semantic Web Tries to Enforce Meaning from the Top This fallacy claims that the Semantic Web, enforces meaning on users through its standards OWL and RDF(S). The repost to this fallacy is easy. The only meaning that OWL and RDF(S) enforce is the meaning of the connectives in a 3

e.g. http://www.shirky.com/writings/semantic syllogism.html and http:// www.csdl.tamu.edu/∼marshall/mc-semantic-web.html

Semantic Web Research Anno 2006

3

language that users can use to express their own meaning. The users are free to to choose their own vocabulary, and to assign their own meaning to terms in this vocabulary, to describe whatever domain of their choice. OWL and RDF(S) are entirely neutral in this. The situation is comparable to HTML: HTML does not enforce the lay-out of web-pages “from the top”. All HTML enforces is the language that people can use to describe their own lay-out. And HTML has shown that such an agreement on the use of a standardised language (be it HTML for the lay-out of web-pages, or RDF(S) and OWL for their meaning) is a necessary ingredient for world-wide interoperability. Fallacy 2: The Semantic Web Requires Everybody to Subscribe to a Single Predeﬁned Meaning for the Terms They Use. Of course, the meaning of terms cannot be predeﬁned for global use. Of course, meaning is ﬂuid and contextual. The motto of the Semantic Web is not the enforcement of a single ontology. It’s motto is rather “let a thousand ontologies blossom”. That is exactly the reason why the construction of mappings between ontologies is such a core topic in the Semantic Web community (see [2,3,4] for some surveys). And such mappings are expected to be partial, imperfect and context-dependent. Fallacy 3: The Semantic Web will Require Users to Understand the Complicated Details of Formalised Knowledge Representation. Indeed some of the core technology of the Semantic Web relies on intricate details of formalised knowledge representation. The semantics of RDF Schema and OWL, and the layering of the subspecies of OWL are diﬃcult formal matters. The design of good ontologies is a specialised are of Knowledge Engineering. But for most of the users of (current and future) Semantic Web applications, such details will be entirely “under the hood”, just as the intricacies of CSS and (X)HTML are under the hood of the current Web. Navigation or personalisation engines can be powered by underlying ontologies, expressed in RDF Schema or OWL, without the user ever being confronted with the ontology, let alone its representation language. Fallacy 4: The Semantic Web People will Require the Manual Markup of all Existing Web-pages It’s hard enough for most web-site owners to maintain the human-readable content of their site. They will certainly not maintain a second parallel version in which they will have to write a machine-accessible version of the same information in RDF or OWL. If this were the case, that would indeed spell bad news for the Semantic Web. Instead, Semantic Web applications rely on large-scale automation for the extraction of such semantic markup from the sources themselves. This will often be very lightweight semantics, but for many applications, that has shown to be enough.

4

F. van Harmelen

Notice that this fallacy mostly aﬀects interpretation 2 of the Semantic Web (previous section), since massive markup in the “Web of data” is much easier: the data is already available in (semi-)structured formats, and is often already organised by database schema’s that can provide the required semantic interpretation.

3

Current Status

In this section, we will brieﬂy survey the current state of work on the Semantic Work in two ways. First we will try to assess the progress that has been made in answering four key questions on which the success of the Semantic Web relies. Secondly, we will give a quick overview of the main areas in which Semantic Web technology is currently being adopted. 3.1

The Four Main Questions

Question 1: Where does the Meta-data Come from? As pointed out in our Fallacy No. 4, much of the semantic meta-data will have to come from Natural Language Processing and Machine Learning technology. And indeed, these technologies are delivering this promise. It is now possible with oﬀ-the-shelve technology to produce semantic markup for very large corpora of web-pages (millions of pages) by annotating them with terms from very large ontologies (hundreds of thousands of terms), at a suﬃciently quality precision and recall to drive semantic navigation interfaces. Our own work on the DOPE prototype is only one of many examples that can be given: 5 million web-pages indexed with an ontology of 235.000 concepts, used for query disambiguation, narrowing, widening and semantic clustering of query results [5]. More recently (and for many in the Semantic Web community somewhat unexpected) is the capability of social communities to do exactly what Fallacy 4 claims is impossible: providing large amounts of human-generated markup. Millions of images, hundreds of millions of manually provided with meta-data tags on some of the most popular “Web 2.0” sites. Question 2: Where do the ontologies come from? As pointed out by [6], the term ontology as used by the Semantic Web community now covers a wide array of semantic structures, from lightweight hierarchies such as MeSH4 to heavily axiomatised ontologies such as GALEN5 . The lesson of a decade worth of Knowledge Engineering and half a decade of Semantic Web research is that indeed the world is full of such “ontologies”: companies have product catalogues, organisations have internal glossaries, scientiﬁc communities have their public meta-data schemata. These have typically been constructed for other purposes, most often pre-dating the Semantic Web, but very useable as material for Semantic Web applications. 4 5

http://www.nlm.nih.gov/mesh/ http://www.opengalen.org/

Semantic Web Research Anno 2006

5

There are also signiﬁcant advances in the area of ontology-learning, although results there remain mixed: obtaining the concepts of an ontology is feasible given the appropriate circumstances, but placing them in the appropriate hierarchy with the right mutual relationships remains a topic of active research. Question 3: What to do with Many Ontologies? As stated in our rebuttal to fallacy No. 2, the Semantic Web crucially relies on the possibility to integrate multiple ontologies. This is known as the problem of ontology alignment, ontology mapping or ontology integration, and is indeed one of the most active areas of research in the Semantic Web community. Excellent surveys of the current state of the art are provided by [2,3,4]. A wide array of techniques is deployed for solving this problem, with ontology mapping techniques based on natural language technology, based on machinelearning, on theorem-proving, on graph-theory, on statistics, etc. Although encouraging results are obtained, this problem is by no means solved, and automatically obtained results are not yet good enough in terms of recall and precision to drive many of the intended Semantic Web use-cases. Consequently, ontology-mapping is seen by many as the Achilles Heel of the Semantic Web. Question 4: Wheres the “Web” in the Semantic Web? The Semantic Web has sometimes been criticised as being too much about “semantic” (i.e. large-scale distributed knowledge-bases), and not enough about “web”. This was perhaps true in the early days of Semantic Web developments, where there was a focus on applications in rather circumscribed domains like intranets. This initial emphasis is still visible to a large extent: many of the most successful applications of Semantic Web technology are indeed on company intranets. Of course the main advantage of such intranet-applications are that the ontology-mapping problem can to a large extent be avoided. Recent years have seen a resurgence in the Web-aspects of Semantic Web applications. A prime example of this is the deployment of FOAF technology6 , and of semantically organised P2P systems (see e.g. the collection of work in [7]). Of course the Web is more than just textual documents: non-textual media such as images and videos are an integral part of the Web. For the application of Semantic Web technology to such non-textual media we must for the foreseeable future rely on human-generated semantic markup (as discussed above), given the diﬃculty of automatically generating meaningful markup for such media. Main Application Areas It is beyond the scope of this brief paper to give an in-depth and comprehensive overview of all Semantic Web applications. We will limit ourselves to a bird’s eye survey. 6

http://www.foaf-project.org/

6

F. van Harmelen

Looking at industrial events either dedicated events7 or co-organised with the major international scientiﬁc Semantic Web conferences, we observe the following. A healthy uptake of Semantic Web technologies is beginning to take shape in the following areas: – – – –

knowledge management, mostly in intranets of large corporations data-integration (Boeing, Verison and others) e-Science, in particular the life-sciences8 convergence with Semantic Grid

If we look at the proﬁles of companies active in this area, we see a distinct transition from small start-up companies such as Aduna, Ontoprise, Network Inference, Top Quadrant (to name but a few) to large vendors such as IBM (their Snobase ontology Management System9 , HP (with their popular Jena RDF platform10 , Adobe (with their RDF-based based XMP meta-data framework) and Oracle (now lending support for RDF storage and querying in their prime data-base product). However, besides the application areas listed above, there is also a noticable lack of uptake in some other areas. In particular, promises in the areas of – personalisation, – large-scale semantic search (i.e. on the scale of the World Wide Web, not limited to intranets), – mobility and context-awareness are largely unfulﬁlled. A pattern that seems to emerge between the succes unsuccessfull application areas is that the succesfull areas are all aimed at closed communities (employees of large corporations, scientists in a particular area), while the applications aimed at the general public are still in the laboratory phase at best. The underlying reason for this could well be as discussed above, namely the diﬃculty of the ontology mapping.

4

Challenges

Many of the challenges that we outlined in an earlier paper [8] are in the meantime active areas of research: – scale (with inference and storage technology are now scaling to the order of billions of RDF triples, – ontology evolution and change – ontology mapping, as outlined above. 7 8

9 10

e.g. http://www.semantic-conference.com/ see e.g. http://www2006.org/speakers/stephens/stephens.ppt for some state-ofthe-art industrial work. http://www.alphaworks.ibm.com/tech/snobase http://jena.sourceforge.net/

Semantic Web Research Anno 2006

7

However, a number of items on the research agenda are hardly tackled, but do have a crucial impact on the feasibility of the Semantic Web vision. In particular: – the mutual interaction between machine-processable representations and the dynamics of social networks of human users – mechanisms to deal with trust, reputation, integrity and provenance in a semi-automated way – inference and query facilities that are suﬃciently robust to work in the face of limited resources (be it either computation time, network latency, memory or storage-space), and that can make intelligent trace-oﬀ decisions between resource use and output-quality

References 1. Marshall, C.C., Shipman, F.M.: Which semantic web? In: HYPERTEXT ’03: Proceedings of the fourteenth ACM conference on Hypertext and hypermedia, New York, NY, USA, ACM Press (2003) 57–66 2. Kalfoglou, Y., Schorlemmer, M.: Ontology mapping: the state of the art. The Knowledge Engineering Review Journal (KER) (2003) 1–31 3. Rahm, E., Bernstein, P.: A survey of approaches to automatic schema matching. The VLDB Journal (2001) 334–350 4. Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. Journal on Data Semantics (2005) 146–171 5. Stuckenschmidt, H., van Harmelen, F., de Waard, A., Scerri, T., Bhogal, R., van Buel, J., Crowlesmith, I., Fluit, C., Kampman, A., Broekstra, J., van Mulligen, E.: Exploring large document repositories with rdf technology: The dope project. IEEE Intelligent Systems 19 (2004) 34–40 6. Jasper, R., Uschold, M.: A framework for understanding and classifying ontology applications. In: Proceedings 12th Int. Workshop on Knowledge Acquisition, Modelling, and Management KAW. (1999) 4–9 7. Staab, S., Stuckenschmidt, H.: Semantic Web and Peer-to-peer: Decentralized Management and Exchange of Knowledge and Information. Springer (2005) 8. van Harmelen, F.: How the semantic web will change kr: challenges and opportunities for a new research agenda. The Knowledge Engineering Review 17 (2002) 93–96

A Research Agenda for Agent-Based Service-Oriented Architectures Michael N. Huhns Department of Computer Science and Engineering University of South Carolina, Columbia, SC 29208, USA [email protected] http://www.cse.sc.edu/~ huhns

Abstract. Web services, especially as fundamental components of service-oriented architectures, are receiving a lot of attention. Their great promise, however, has not yet been realized, and a possible explantion is that signiﬁcant research and engineering problems remain. We describe the problems, indicate likely directions and approaches for their solution, present an agenda for the deployment of such solutions, and explain the beneﬁts of the resultant deployment. It is our strong expectation that Web services will eventually have an agent basis, which would be needed to address the problems.

1

Introduction

The latest paradigm for structuring large-scale applications is a service-oriented architecture (SOA), which involves the linking of small functional services to achieve some larger goal. As the central concept in service-oriented architectures, Web services provide a standardized network-centric approach to making the functionality available in an encapsulated form. It is worth considering the major beneﬁts of using standardized services. Clearly anything that can be done with services can be done without. So the following are some reasons for using services, especially in standardized form. – Services provide higher-level abstractions for organizing applications in largescale, open environments. Even if these were not associated with standards, they would be helpful as we implemented and conﬁgured software applications in a manner that improved our productivity and improved the quality of the applications that we developed. – Moreover, these abstractions are standardized. Standards enable the interoperation of software produced by diﬀerent programmers. Standards thus improve our productivity for the service use cases described above. – Standards make it possible to develop general-purpose tools to manage the entire system lifecycle, including design, development, debugging, monitoring, and so on. This proves to be a major practical advantage, because without signiﬁcant tool support, it would be nearly impossible to create and ﬁeld robust systems in a feasible manner. Such tools ensure that the components M. Klusch, M. Rovatsos, and T. Payne (Eds.): CIA 2006, LNAI 4149, pp. 8–22, 2006. c Springer-Verlag Berlin Heidelberg 2006

A Research Agenda for Agent-Based Service-Oriented Architectures

9

developed are indeed interoperable, because tool vendors can validate their tools and thus shift part of the burden of validation from the application programmer. – The standards feed other standards. For example the above basic standards enable further standards, e.g., dealing with processes and transactions. To realize the above advantages, SOAs impose the following requirements: Loose coupling. No tight transactional properties should generally apply among the components. In general, it would not be appropriate to specify the consistency of data across the information resources that are parts of the various components. However, it would be reasonable to think of the high-level contractual relationships through which the interactions among the components are speciﬁed. Implementation neutrality. The interface is what matters. We cannot depend on the details of the implementations of the interacting components. In particular, the approach cannot be speciﬁc to a set of programming languages. Flexible conﬁgurability. The system is conﬁgured late and ﬂexibly. In other words, the diﬀerent components are bound to each other late in the process and the conﬁguration can change dynamically. Long lifetime. to be useful to external applications, components must have a long lifetime. Morever, since we are dealing with computations among autonomous heterogeneous parties in dynamic environments, we must always be able to handle exceptions. This means that the components must exist long enough to be able to detect any relevant exceptions, to take corrective action, and to respond to the corrective actions taken by others. Components must exist long enough to be discovered, to be relied upon, and to engender trust in their behavior. Granularity. The participants in an SOA should be understood at a coarse granularity. That is, instead of modeling actions and interactions at a detailed level, it would be better to capture the essential high-level qualities that are (or should be) visible for the purposes of business contracts among the participants. Coarse granularity reduces dependencies among the participants and reduces communications to a few messages of greater signiﬁcance. Teams. Instead of framing computations centrally, it would be better to think in terms of how computations are realized by autonomous parties. In other words, instead of a participant commanding its partners, computation in open systems is more a matter of business partners working as a team. That is, instead of an individual, a team of cooperating participants is a better modeling unit. A team-oriented view is a consequence of taking a peer-topeer architecture seriously. Web services, viewed as encapsulated and well deﬁned pieces of software functionality accessible to remote applications via a network, are expected to be a fundamental aspect of many future software applications. Many claims have been made about the beneﬁts of Web services for enterprise information systems and

10

M.N. Huhns

next-generation network-based applications, but the widespread availability and adoption of Web services have not yet occurred. The development, deployment, and proliferation of other new computing technologies can be seen as having occurred in stages as developers and users become familiar with the features of the technology and learn how to exploit them. The development of Web services is likely to progress according to the following four stages. Stage 1. The ﬁrst stage in the development of Web services, which is the stage that we are in currently, is that a few speciﬁc Web services will be available, mostly on intranets. There will be little or no semantics describing them. Because of this, their discovery will occur manually, their invocation will be hardcoded, and their composition with other Web services will be either nonexistent or done manually. There will be no fees for their use. The resultant applications that make use of the Web services will be brittle and static, but large ones can be crafted relatively quickly. There will be some examples of unexpected uses and utilities. Stage 2. The second stage in the development of Web services will be characterized by many services being available across the Internet. There will be suﬃcient semantics, via the use of standardized keywords for narrow domains, to enable semi-automatic discovery. Compostion of services will still be arranged manually. There might be some fees for use, but they will be negotiated oﬀ-line by humans. The resultant applications might be dynamic via the substitution of Web services that are explicitly mirrored or via the use of alternative equivalents, most likely where the service functionality is common and straightforward. The desirability of this form of dynamism, and its concomitant robustness, might serve as a major motivation for the further proliferation of Web services and for improved semantics to enable dynamic discovery and a limited form of composition. The scope of the dynamic composition would be one-to-one replacement for a malfunctioning component service. Stage 3. In the third stage, many Web services will be available, each with a rich semantic description of its functionality. The semantics will enable Web services to be discovered and invoked dynamically and on-demand. Stage 4. During the fourth stage, some of the many available Web services will be active, instead of passive, and will have many of the capabilities that characterize software agents. By being active, they will be able to bid for their use in applications, requiring them to be able to negotiate over Quality-of-Service (QoS) and non-functional semantics. This will include negotiation over fees. In some applications, a candidate Web service could be tried and tested for appropriate functionality and QoS. Comparable services could not only substitute for each other, but also be used redundantly for improved robustness. Moreover, services would self-organize, possible on-demand, into service teams to provide aggregate functionality.

A Research Agenda for Agent-Based Service-Oriented Architectures

11

The above four stages of development are driven by limitations of current Web services. These can best be described and understood in the context of the well known “Web service triangle,” as shown in Figure 1. In a subsequent section, we consider each of the triangle’s vertices and edges and point out the desired enhancements, and thus research, needed for advancement to the next stages.

Service Broker

Publish (WSDL)

Service Provider

Find (UDDI)

Bind (Soap/HTTP)

Service Requestor

Fig. 1. The familiar Web service triangle. Its limitations can be revealed by considering each of its vertices and edges.

2

An Example of Current SOA Success

To put the above stages in perspective, let’s consider a currently successful example of a service-oriented architecture, that of Amazon.com, which serves to explain the great interest in this approach to application development. Originally, Amazon.com consisted of a large monolithic application that implemented all of the functionality that was made available through its Web site, including the front-end display, back-end database, and business logic [7]. But the monolithic reached a point where it could not be easily scaled to handle the volume of transactions that had to be processed. So, Amazon reengineered it into what became a service-oriented architecture. Service orientation meant encapsulating the data with the business ligic that operates on it, with the only access through a well deﬁned interface. The services did not share data and did not allow any direct access to the underlying database. The result is hundreds of services and a smaller number of servers that aggregate information from the services. The servers render the Web pages

12

M.N. Huhns

for the Amazon.com application, as well as serve the customer service application, the seller interface, the external interface to Amazon’s Web services, and Amazon-hosted third-party applications. When a user visits the Amazon site, over 100 services are typically invoked in constructing the user’s personalized Web page. The end result is that Amazon can build complex applications quickly out of primitive services, and then scale them appropriately. Moreover, third parties can use the services to build their own applications, primarily for e-commerce, but some for applications unforeseen by Amazon. For example, the Web site “The Amazing Baconizer” invokes Amazon’s recommendation seervice for entertainment purposes to list the connections between two items. The connections are done by looking at “people who bought item A also bought item B.” Here is a sample result for the connection between the book “Surely You’re Joking, Mr. Feynman!” and the DVD “Real Genius”: “Surely You’re Joking, Mr. Feynman!” =⇒ “Real Genius” (11 hops): “Surely You’re Joking, Mr. Feynman!” – R. Feynman =⇒ “Genius: The Life and Science of Richard Feynman,” – J. Gleick =⇒ . . . =⇒ “Weird Science”(DVD) – John Hughes =⇒ “Top Secret!” (DVD) – Val Kilmer =⇒ “Real Genius” (DVD) – Val Kilmer Another interesting third-party service can be accessed from a camera phone. When shopping, a user can take a photo of the bar code for a product, send it to the service, and receive via amazon’s services reviews, information on comparable products, and the price.

3

Needed Research for Each Aspect of Web Services

Figure 1 shows the generic architecture for Web services. Although this is a simple picture, it radically alters many of the problems that must be solved in order for the architecture to become viable on a large scale. – To publish eﬀectively, we must be able to specify services with precision and with greater structure. This is because the service would eventually be invoked by parties that are not from the same administrative space as the provider of the service and diﬀerences in assumptions about the semantics of the service could be devastating. – From the perspective of the registry, it must be able to certify the given providers so that it can endorse the providers to the users of the registry. – Requestors of services should be able to ﬁnd a registry that they can trust. This opens up challenges dealing with considerations of trust, reputation, incentives for registries and, most importantly, for the registry to understand the needs of a requestor.

A Research Agenda for Agent-Based Service-Oriented Architectures

13

– Once a service has been selected, the requestor and the provider must develop a ﬁner-grained sharing of representations. They must be able to participate in conversations to conduct long-lived, ﬂexible transactions. Related questions are those of how a service level agreement (SLA) can be established and monitored. Success or failure with SLAs feeds into how a service is published and found, and how the reputation of a provider is developed and maintained. Most of the needed enhancements are related to the scaling of Web services, not only to larger applications but also to more complex environments [1]. That is, there will be a multiplicity of requestors, providers, and registries, whereas the current conception of Web services focuses on just one of each. There will also be more complex interactions than just a simple remote-procedure call to a service: the interactions will be characterized as peer-to-peer, rather than client-server [3]. When there are multiple requestors, then a service provider might be able to share the results of a computation among the requestors, and the requestors might be able to negotiate a “group rate.” Of course, this requires the requestors to form a cohesive group and to have a negotiation ability. Multiple equivalent providers present both a problem and an opportunity. The problem is that a requestor must have and apply a means to choose among them. This would likely require an ability to negotiate over both functional and non-functional attributes (qualities) of the services. The opportunity is that alternatives can yield increased robustness (described more fully below). Multiple service registries present problems for providers in deciding where to advertise their services and for requestors in deciding where to search for services [4,6]. Also, each registry might employ diﬀerent semantics and organizations of domain concepts. Other limitations represented by the current simple model for Web services are – A Web service knows only about itself—not about its users, clients, or customers – Web services are not designed to use and reconcile ontologies used by each other or by their clients – Web services are passive until invoked; they cannot provide alerts or updates when new information becomes available – Web services do not cooperate with each other or self-organize, although they can be composed by external systems. Another fundamental problem arises when Web services are composed. Consider the simple example in Figure 2 of one Web service that provides stock quotes in dollars and a second that converts dollars into another currency. Current Web services must be invoked sequentially by a central controller. A signiﬁcantly better model is shown in Figure 3, where an interaction protocol under development, WSDL-P, would provide for a continuation by passing a description of an overall workﬂow through each Web service participating in the workﬂow. In this way, the composed services would interact directly, rather than through a central intermediary.

14

M.N. Huhns

Currency Converter Web Service

Stock Quote Web Service

1. WSDL “ IBM”

3. WSDL “$100” 2. “$100”

4. “ ¥800”

Client Application Fig. 2. Composed Web services, which are described traditionally by WSDL

Stock Quote Web Service

2. WSDL-P “$100” + OWL-S

1. WSDL-P “ IBM” + OWL-S

Currency Converter Web Service 3. “¥800”

Client Application Fig. 3. WSDL-P: Next-Generation Composition. An OWL-S (or BPEL4WS) description of the workﬂow is communicated through the services

Solutions to the above described problems are under investigation by many research teams. The keys to the next-generation Web are cooperative services, systemic trust, and understanding based on semantics, coupled with a declarative agent-based infrastructure. These concepts are elaborated in the next sections, beginning with the notion of commitments as the governing principle for the complex interactions inherent in the later stages of SOA developments.

A Research Agenda for Agent-Based Service-Oriented Architectures

4

15

Commitments

For services to apply naturally in open environments, they should be modeled as being autonomous. Autonomy is a natural characteristic of agents, and it is also a characteristic of many envisioned Internet-based services. Among agents, autonomy generally refers to social autonomy, where an agent is aware of its colleagues and is sociable, but nevertheless exercises its independence in certain circumstances. Autonomy is in natural tension with coordination or with the higher-level notion of a commitment. To be coordinated with other agents or to keep its commitments, an agent must relinquish some of its autonomy. However, an agent that is sociable and responsible can still be autonomous. It would attempt to coordinate with others where appropriate and to keep its commitments as much as possible, but it would exercise its autonomy in entering into those commitments in the ﬁrst place. The ﬁrst step to structuring and formalizing interactions among service providers and requestors is to introduce the notion of directed obligations, which are obligations directed from one party to another. This is certainly a useful step. Dignum and colleagues describe a temporal deontic logic that helps specify obligations and constraints so that a planner can take deadlines into account while generating plans [2]. However, the approach is based on the notion of obligations, and it does not give operational methods for obligations. Once a deadline has passed and a certain rule has been violated, the logic has nothing to say about the eﬀects on the system. Nevertheless, this approach is semantically rich and detailed in the kinds of deadlines and constraints it allows agents to model. For example, the deadline “as soon as possible,” can be modeled. However, for virtual enterprises and business protocols, it is generally the case that the obligation of one party to another is bounded by the scope of their ongoing interaction. In other words, obligations derived from a virtual enterprise may last no longer than the virtual enterprise in question. Further, there is always the element of conﬂict, which means that the parties to a contract may be in the need for some adjudication. These considerations suggest that there is an organizational structure to the obligations, which bounds the scopes of the obligations. The notion of commitments (for historical reasons, sometimes referred to as social commitments) takes care of the above considerations. Commitments are a legal abstraction, which subsume directed obligations. Importantly, commitments (1) are public, and (2) can thus be used as a basis for compliance. Commitments support the following key properties that make them a useful computational abstraction for service-oriented architectures. Multiagency. Commitments associate one agent or party with another. The party that “owes” the commitment is called the debtor and the other party is called the creditor. Each commitment is directed from its debtor to its creditor. The directionality is simply a representational convenience. In practice, commitments would arise in interrelated sets. For example, a typical business

16

M.N. Huhns

contract would commit one party to pay another party and the second party to deliver goods to the ﬁrst party. Scope. Commitments arise within a well-deﬁned scope. This scope functions as the social context of the commitment. In other words, the scope is itself modeled as a multiagent system within which the debtor and creditor of the given commitment interact. For example, the parties to a business contract can be understood as forming and acting in a multiagent system in which they create their respective commitments and act on them. The multiagent system may have a short or a long lifetime depending on the requirements of the application. Conceivably, the multiagent system for a one-oﬀ interaction would be dissolved immediately, whereas some multiagent systems may even last longer than the speciﬁc agents that belong to them. Manipulability. Commitments can be acted upon and modiﬁed. In particular, commitments may be revoked. If we were to prevent modifying or revoking commitments, we would end up ruling out some of the most interesting scenarios where commitments can be applied. For example, irrevocability would be too limiting for the kinds of open applications where service-oriented architectures make sense. Irrevocability would prevent considering errors and exceptions that may occur outside of the administrative domain of the given business partner. For instance, it may simply be impossible for a vendor to deliver the promised goods on time if the vendor’s factory burns down or there are diﬃculties with shipping. However, we must be careful that commitments are not revoked arbitrarily, which would make them worthless. When restrictions (sensitive to a given context) are imposed on the manipulation of commitments, they can support the coherence of computations. Services, although collaborative, retain their autonomy. They can exercise their local policies for most decisions and can be considered as being constrained only by their commitments. 4.1

A Formalization of Commitments

We write commitments using a predicate C. A commitment has the form C(x, y, p, G) where x is its debtor, y its creditor, p the condition the debtor will bring about, and G a multiagent system, which serves as the organizational context for the given commitment. A commitment has a simple form, e.g., C(b, s, pay(b, s, $10), D), where a buyer b commits to pay $10 to a seller s a seller within the context of a particular business deal D between b and s. 4.2

Operations on Commitments

It helps to treat commitments as an abstract data type. This data type associates a debtor, a creditor, a condition, and a context. The following are then natural for commitments.

A Research Agenda for Agent-Based Service-Oriented Architectures

17

– create(x, c) establishes the commitment c in the system. This can only be performed by c’s debtor x. For example, x promises to pay $10 to y. – cancel(x, c) cancels the commitment c. This can be performed only by c’s debtor x, for example, x reneges on its promise to pay $10. Generally, making another commitment compensates cancellation. – release(y, c) releases c’s debtor x from commitment c. This only can be performed by the creditor y or a higher authority. For example, x decides to waive receiving the $10, or the government steps in to say that the agreement is null and void. – assign(y, z, c) replaces y with z as c’s creditor. For example, x is now committed to pay $10 to y’s friend z. – delegate(x, z, c) replaces x with z as the debtor for c. For example, now x’s friend is committed to pay $10 to y. – discharge(x, c) means that c’s debtor x fulﬁlls the commitment. For example, x actually pays $10 to y or the assigned creditor. Create and discharge are obvious; delegate and assign add some ﬂexibility to commitments and are also obvious. Cancel and release remove a commitment from being in eﬀect. Cancel is essential to reﬂect the autonomy of an agent; just because it made a commitment does not mean that the commitment is irrevocable. However, if commitments could be wantonly canceled, there would be no point in having them, so cancellations of commitments must be suitably constrained. Release helps capture various subtleties of relationships among business partners. A partner may decide not to insist that another party discharge its commitments. Alternatively, the organizational context within which the parties interact may ﬁnd that a commitment should be eliminated. For example, ordinarily a buyer is expected to pay for goods and a pharmacist is expected to ship medicines that are paid for. However, if the goods arrive damaged then the buyer is released from paying for them (but must return them instead); if the medicine prescription turns out to be invalid, the pharmacist is released from the commitment to ship the medications.

5

Robust Services Via Agent-Based Redundancy

A major driver behind an agent basis for Web services is the demand for robustness. All approaches to robustness rely on some form of redundancy, and Web services are a natural source of redundancy for software applications. Software problems are typically characterized in terms of bugs and errors, which may be either transient or omnipresent. The general approaches for dealing with them are: (1) prediction and estimation, (2) prevention, (3) discovery, (4) repair, and (5) tolerance or exploitation. Bug estimation uses statistical techniques to predict how many ﬂaws might be in a system and how severe their eﬀects might be. Bug prevention is dependent on good software engineering techniques and processes. Good development and run-time tools can aid in bug discovery, whereas repair and tolerance depend on redundancy.

18

M.N. Huhns

Indeed, redundancy is the basis for most forms of robustness. It can be provided by replication of hardware, software, or information, e.g., by repetition of communication messages. Redundant code cannot be added arbitrarily to a software system, just as steel cannot be added arbitrarily to a bridge. A bridge is made stronger by adding beams that are not identical to ones already there, but that have equivalent functionality. This turns out to be the basis for robustness in service-oriented systems as well: there must be services with equivalent functionality, so that if one fails to perform properly, another can provide what is needed. The challenge is to design service-oriented systems so that they can accommodate the additional services and take advantage of their redundant functionality. We hypothesize that agents are a convenient level of granularity at which to add redundancy and that the software environment that takes advantage of them is akin to a society of such agents, where there can be multiple agents ﬁlling each societal role [8]. Agents by design know how to deal with other agents, so they can accommodate additional or alternative agents naturally. Fundamentally, the amount of redundancy required is well speciﬁed by information theory. If we want a system to provide n functionalities robustly, we must introduce m×n agents, so that there will be m ways of producing each functionality. Each group of m agents must understand how to detect and correct inconsistencies in each other’s behavior, without a ﬁxed leader or centralized controller. If we consider an agent’s behavior to be either correct or incorrect (binary), then, based on a notion of Hamming distance for error-correcting codes, 4 × m agents can detect m − 1 errors in their behavior and can correct (m − 1)/2 errors. Redundancy must also be balanced with complexity, which is determined by the number and size of the components chosen for building a system. That is, adding more components increases redundancy, but also increases the complexity of the system. An agent-based system can cope with a growing application domain by increasing the number of agents, each agent’s capability, or the computational and infrastructure resources that make the agents more productive. That is, either the agents or their interactions can be enhanced, but to maintain the same redundancy m, they would have to be enhanced by a factor of m. N-version programming, also called dissimilar software, is a technique for achieving robustness ﬁrst considered in the 1970s. It consists of N separately developed implementations of the same functionality. Although it has been used to produce several robust systems, it has had limited applicability, because (1) N independent implementations have N times the cost, (2) N implementations based on the same ﬂawed speciﬁcation might still result in a ﬂawed system, and (3) each change to the speciﬁcation will have to be made in all N implementations. Database systems have exploited the idea of transactions: an atomic processing unit that moves a database from one consistent state to another. Consistent transactions are achievable for databases because the types of processing done are regular and limited. Applying this idea to software execution requires that the state of a software system be saved periodically (a checkpoint) so the system can return to that state if an error occurs.

A Research Agenda for Agent-Based Service-Oriented Architectures

5.1

19

Architecture and Process

Suppose there are a number of services, each with strengths, weaknesses, and possibly errors. How can the services be combined so that the strengths are exploited and the weaknesses or ﬂaws are compensated or covered? Three general approaches are evident in Figure 4. First, a preprocessor could choose the best services to perform a task, based on published characteristics of each service. Second, a postprocessor could choose the best result out of several executing services. Third, the services could decide as a group which ones should perform the task. Single Task

Choose service based on: (1) functionality, (2) QoS

Service #1

Service #2

...

Service #N

Compare Results and Select Best

Single Result Fig. 4. Improving robustness by combining multiple implementations of a service

The diﬃculties with the ﬁrst two approaches are (1) the preprocessor might be ﬂawed, (2) it is diﬃcult to maintain the preprocessor as services are added or changed, and (3) the postprocessor wastes resources, because several services work on the data and their results have to be compared. The third approach requires distributed decision-making, which is not an ability of conventional Web services. What generic ability could be added to a service to enable it to participate in a distributed decision? The generic capability has the characteristics of an agent, so distributing the centralized functions into the diﬀerent modules creates a multiagent system. Each agent would have to know its role as well as (1) something about its own service, such as its time and space

20

M.N. Huhns

complexity, and input and output data structures; (2) the complexity and reliability of other agents; and (3) how to communicate, negotiate, compare results, and manage reputations and trust. 5.2

Experimental Results

Huhns and colleagues collected one set of 25 algorithms for reversing a doubly linked list and another set for sorting a list. Diﬀerent novice programmers wrote each algorithm. For sorting, no speciﬁcations were given to the programmers (beyond that the problem was sorting), so the algorithms all have diﬀerent data and performance characteristics. For list reversing, the class structure (i.e., method signatures) was speciﬁed, so the diﬀerences among the algorithms are in performance and correctness. Each algorithm was converted into an agent, composed of the algorithm written in Java and a wrapper written in Jade. The wrapper knows only about the signature of its algorithm, and nothing about its inner workings. Our experiments veriﬁed that the same wrapper can be used for both the sorting and list-reversing domains. We also veriﬁed our hypothesis that more algorithms give better results than any one alone. Further, we investigated both a distributed preprocessor and a centralized postprocessor for combining the agents’ functionality, and found that the postprocessor is generally better, but performs worse for large data sets or selected algorithms with long execution times. The eventual outcome for application development is that service developers will spend more time on functionality development and less on debugging, because diﬀerent services will likely have errors in diﬀerent places and can cover for each other.

6

Conclusion and Agenda

Service-oriented computing (SOC) represents an emerging class of approaches with multiagent-like characteristics for developing systems in large-scale open environments. Indeed, SOC presents several challenges that cannot be tackled without agent concepts and techniques. Viewed in this light, SOC oﬀer many ways in which to change the face of computing. As services become increasingly like agents and their interactions become increasingly dynamic, theyll begin to do more than just manage information in explicitly programmed ways. In particular, services acting in concert can function as computational mechanisms in their own right, thus signiﬁcantly enhancing our ability to model, design, build, and manage complex software systems. Think of such MASs as providing a new approach for constructing complex applications wherein developers concentrate on high-level abstractions, such as overall behavior and key conceptual structures (the active entities, their objectives, and their interactions), without having to go further into individual agents details or interactions. This vision becomes more compelling as the target environments become more

A Research Agenda for Agent-Based Service-Oriented Architectures

21

– populous (a monolithic model is intractable, whereas developers can construct an MAS modularly) – distributed (pulling information to a central location for monitoring and control is prohibitive, whereas techniques based on interaction among agents and the emergence of desired system-level behaviors are much easier to manage) – dynamic (an MAS can adapt in real time to changes in the target system and the environment in which it is embedded). Table 1. Reasons for Complex System Development Based on Multiagent ServiceOriented Systems Multiagent System Properties Beneﬁts for System Development Autonomous, objective-oriented behavior; Autonomous, active functionality that agent-oriented decomposition adapts to the users needs; reuse of whole subsystems and ﬂexible interactions Dynamic composition and customization Scalability Interaction abstractions; statistical or Friction-free software; open systems; inprobabilistic protocols teractions among heterogeneous systems; move from sophisticated and learned ecommerce protocols to dynamic selection of protocols Multiple viewpoints, negotiation, and col- Robustness and reliability laboration Social abstractions High-level modeling abstractions

Table 1 shows the ways in which MAS properties can beneﬁt the engineering of complex service-oriented systems. Potential applications and application domains that can also beneﬁt from this approach include meeting scheduling, scientiﬁc workﬂow management, distributed inventory control and supply chains, air and ground traﬃc control, telecommunications, electric power distribution, water supplies, and weapon systems.

References 1. Mark Burstein, Christoph Bussler, Tim Finin, Michael Huhns, Massimo Paolucci, Amit Sheth, Stuart Williams, and Michael Zaremba, “A Semantic Web Services Architecture,” IEEE Internet Computing, vol. 9, no. 5, pp. 72–81, September/October 2005. 2. F. Dignum, H. Weigand, and E. Verharen, “Meeting the Deadline: On the Formal Speciﬁcation of Temporal Deontic Constraints,” Foundations of Intelligent Systems, 9th Intl Symp., (ISMIS ’96), vol. 1079, Lecture Notes in Computer Science, Springer, 1996, pp. 243–252. 3. A. Eberhart, “Ad-Hoc Invocation of Semantic Web Services,” Proc. IEEE Intl Conf. Web Services, IEEE CS Press, 2004; www.aifb.uni-karlsruhe.de/WBS/aeb/pubs/ icws2004.pdf.

22

M.N. Huhns

4. K. Sycara et al., “Dynamic Service Matchmaking among Agents in Open Information Environments,” J. ACM SIGMOD Record, Special Issue on Semantic Interoperability in Global Information Systems, vol. 28, no. 1, 1999, pp. 47–53; http://www-2. cs.cmu.edu/ softagents/papers/ACM99-L.ps. 5. M. Singh and M. Huhns, Service-Oriented Computing: Semantics, Processes, Agents, John Wiley & Sons, 2005. 6. K. Sivashanmugam, K. Verma, and A. Sheth, “Discovery of Web Services in a Federated Registry Environment,” Proc. IEEE Intl Conf. Web Services, IEEE CS Press, 2004; http://lsdis.cs.uga.edu/lib/download/MWSDI-ICWS04- ﬁnal.pdf. 7. Werner Vogels, “Learning from the Amazon Technology Platform,” ACM Queue, May 2006, pp. 14–22. 8. R.L. Zavala and M.N. Huhns, “On Building Robust Web Service- Based Applications,” in Extending Web Services Technologies: The Use of Multi-Agent Approaches, L. Cavedon et al., eds., Kluwer Academic, 2004, pp. 293–310.

The Helpful Environment: Distributed Agents and Services Which Cooperate Austin Tate Artificial Intelligence Applications Institute, University of Edinburgh, Appleton Tower, Crichton Street, Edinburgh EH8 9LE, UK [email protected]

Abstract. Imagine a future environment where networks of agents - people, robots and software agents - interact with sophisticated sensor grids and environmental actuators to provide advice, protection and aid. The systems will be integral to clothing, communications devices, vehicles, transportation systems, buildings, and pervasive in the environment. Vehicles and buildings could assist both their occupants and those around them. Systems would adapt and respond to emergencies whether communication were possible or not. Where feasible, local help would be used, with appropriate calls on shared services facilitated whenever this is both possible and necessary. Through this framework requests for assistance could be validated and brokered to available and appropriate services in a highly distributed fashion. Services would be provided to individuals or communities through this network to add value and give all sorts of assistance beyond emergency response aspects. In emergency situations, the local infrastructure would be augmented by the facilities of the responder teams at any level from local police, ambulance and fire response, all the way up to international response. An emergency zone’s own infrastructure could be augmented when necessary by laying down temporary low cost sensor grids and placing specialized devices and robotic responders into the disaster area. These would form the basis for a distributed, adaptable, and resilient “helpful environment” for every individual and organisation at personal, family, business, regional, national and international levels. Keywords: Intelligent agents, distributed systems, collaborative systems, cooperative systems, sensor grids, emergency response.

1 Introduction Imagine a future environment where a network of agents - people, robots and software agents - interact with sophisticated sensor grids and environmental actuators to provide advice, protection and aid. The systems will be integral to clothing, communications devices, vehicles, transportation systems, buildings, and pervasive in the environment. These would form the basis for a distributed, adaptable, and resilient “helpful environment” for every individual and organisation at personal, family, business, regional, national and international levels. In natural disaster-prone areas government legislation, building codes and insurance requirements would ensure that M. Klusch, M. Rovatsos, and T. Payne (Eds.): CIA 2006, LNAI 4149, pp. 23 – 32, 2006. © Springer-Verlag Berlin Heidelberg 2006

24

A. Tate

appropriate sensor/actuator systems were included in future communication devices, vehicles and buildings to assist both their users and those around them. Systems would adapt and respond to emergencies whether communication were possible or not. Where feasible, local help would be used, with appropriate calls on shared services facilitated whenever this is both possible and necessary. Through this framework requests for assistance could be validated and brokered to available and appropriate services in a highly distributed market fashion. Services would be provided to individuals or communities through this network to add value and give all sorts of assistance beyond the emergency response aspects. In emergency situations, the local infrastructure would be augmented by the facilities of the responder teams at any level from local police, ambulance and fire response, all the way up to international response. An emergency zone’s own infrastructure could be augmented on demand by laying down temporary low cost sensor grids and placing specialized devices and robotic responders into the disaster area.

2 Emergency Response Challenges Local or regional governments are often responsible for the event handling, planning, coordination and status reporting involved in responding to an emergency. They must harness capabilities to augment their own by calling on the resources of others when required. The local authority will often have an emergency response centre which, in the event of an emergency, will provide information and support to the public (through emergency phone lines), to the responders and to the decision making authorities. Across a range of emergency response scenarios, we can identify a common set for which intelligent agents might be of assistance: • • • • • • • • •

Sensor data management and fusion Accurate information gathering Correlation and validation Relevant and understandable communication Contact making Requests for assistance and matching to available capabilities Use of Standard Operating Procedures and Alarms Planning and coordination Scale and robustness

But one of the biggest challenges is to help these agents and services and the people using them to communicate and cooperate effectively. There are many instances in which lack of communication and breakdown in coordination has degraded the emergency response and in some cases led to further loss of life and property.

3 AI Challenges There are many AI challenges to be addressed to give such support and to make the vision a reality. Kitano and Tadokoro (2001) outlined some of this in a 50-year programme of work for future rescue robotics in Japan. They have also introduced the

The Helpful Environment: Distributed Agents and Services Which Cooperate

25

annual RoboCup Rescue Simulation competition held to test systems in a simulation of the 1995 Kobe earthquake. There have been several other proposals for "Grand Challenges" in computing and AI which take as their theme emergency response (Safety.net, 2002; I-Rescue Grand Challenges, 2006). As examples, the UK Advanced Knowledge Technologies (AKT) programme (Aktors, 2005) is addressing emergency response challenge problems, and a European follow-on programme called OpenKnowledge (2006) is using emergency response as one of its challenge problems. The FireGrid project is seeking to link sophisticated and large-scale sensor grids and faster than real-time simulations to emergency response coordination systems. We can outline a number of core technologies, many having an essential AI component, which need to be developed, matured and integrated with other systems to make this vision of a connected world a reality. Examples of these technologies include: 1.

2.

3.

4.

5.

Sensors and Information Gathering a. sensor facilities, large-scale sensor grids b. human and photographic intelligence gathering c. information and knowledge validation and error reduction d. Semantic Web and meta-knowledge e. simulation and prediction f. data interpretation g. identification of "need" Emergency Response Capabilities and Availability a. robust multi-modal communications b. matching needs, brokering and "trading" systems c. agent technology for enactment, monitoring and control Hierarchical, distributed, large scale systems a. local versus centralized decision making and control b. mobile and survivable systems c. human and automated mixed-initiative decision making d. trust, security Common Operating Methods a. shared information and knowledge bases b. shared standards and interlingua c. shared human-scale self-help web sites and collaboration aids d. shared standard operating procedures at levels from local to international e. standards for signs, warnings, etc. Public Education a. publicity materials b. self-help aids c. training courses d. simulations and exercises

Running through all these is the need for flexible and extendible representations of knowledge with rapidly altering scope, and with changing versions and refinements. There cannot be a single monolithically agreed representation of all the knowledge that will be involved. The science and technology of ontologies and their management will be vital to sustain this knowledge.

26

A. Tate

The technologies outlined above are drawn from a number of fields, some more mature than others, with each having its own philosophy and assumptions. However, the technological and research advances that are necessary to realize this vision are starting to be made in a number of projects and research programmes which will now be described.

4 I-Rescue The I-Rescue project (I-Rescue, 2005) is exploring the use of AI planning and collaboration methods in rapidly developing emergency response and rescue situations. The overall aim is the creation and use of task-centric virtual organisations involving people, government and non-governmental organisations, automated systems, grid and web services working alongside intelligent robotic, vehicle, building and environmental systems to respond to very dynamic events on scales from local to global. The I-X system and I-Plan planner (Tate et. al., 2004) provide a framework for representing, reasoning about, and using plans and processes in collaborative contexts. An underlying ontology, termed (Tate, 2003), is used as the basis of a flexible representation for the issues/questions to address, nodes/activities to be performed, constraints to be maintained and annotations to be kept. The I-X approach to plan representation and use relates activities to their underlying "goal structure" using rich (and enrichable) constraint descriptions which include the impact that the activities are meant to have on the state of the environment. This allows for more precise and useful monitoring of plan execution, allowing plans to be adjusted or repaired as circumstances change. It can make use of the dynamically changing context and status of the agents and products involved (e.g. through emerging geo-location services for people and products). It also provides for the real-time communication of activities and tasks between both human and automated resources. I-X agents and the underpinning ontology can be used in a range of systems including supportive interfaces for humans and organisations, and potentially in intelligent sensors and robotic devices. It can thus act as a shared mechanism for coordinating these and for providing them with intelligent planning and process support. I-Rescue and I-X systems aim to be part of a future environment in which there are: • • • • • • • •

Multi-level emergency response and aid systems Personal, vehicle, home, organisation, district, regional, national, international levels of assistance Backbone for progressively more comprehensive aid and emergency response Also used for aid-orientated commercial services Robust, secure, resilient, distributed system of systems Advanced knowledge and collaboration technologies Low cost, pervasive sensors, computing and communications Changes in building codes, regulations and practices.

The Helpful Environment: Distributed Agents and Services Which Cooperate

27

5 Coalition Agents Experiment (CoAX) As recent world events have shown, multi-national Coalitions play an increasingly important role in emergency response operations. The overall aim of is to exploit information better; in Coalitions this requires rapid integration of heterogeneous information handling and command systems, enabling them to inter-operate and share information coherently. However, Coalitions today suffer from labour-intensive information collection and co-ordination, and ‘stove-piped’ systems with incompatible representations of information. The Coalition Agents Experiment (CoAX, 2006, Allsop et al., 2003) was an international collaborative research effort involving 30 organisations in four countries. It brought together a wide range of groups exploring agent technologies relevant to multi-national and multi-agency operations in the context of a peace-keeping scenario set in a fictional country – Binni (Rathmell, 1999). The principal research hypothesis was that the software agent technology and principles of the Semantic Web could help to initially construct and then use and maintain loosely coupled systems for complex and very dynamically changing Coalition operations (e.g. as in Wark et al., 2003). CoAX carried out a series of technology demonstrations based on a realistic Coalition scenario. These showed how agents and associated technologies facilitated run-time interoperability across the Coalition, adaptive and agile responses to unexpected events, and selective sharing of information between Coalition partners.

6 Coalition Search and Rescue Task Support (CoSAR-TS) Search and rescue operations by nature require the kind of rapid dynamic composition of available policy-constrained services making it a good experimental basis for intelligent agent Semantic Web technologies. Semantic Web use within agents in CoAX was taken further in the CoSAR-TS project which also used the CoAX Binni scenario (Rathmell, 1999), and events which immediately followed on from those in the CoAX demonstrations. The KAoS agent domain management framework (Bradshaw et al., 1997) was used to describe the agent domains and the policies under which they interoperate. The project showcases intelligent agents and artificial intelligence planning systems working in a distributed fashion, with dynamic policies originating from various groups and individuals governing who is permitted or obligated to do what. The agents use Semantic Web services to dynamically discover medical information and to find local rescue resources. Semantic Web information access uses the OWL language (2004) and the rescue services are described in OWLS (2005). I-X (Tate et al., 2002) was used as a task support, planning and execution framework to connect the various participants and services used. In later phases of the work, an exploration of web services composition using I-X’s planner (I-Plan) was also included (Uszok et al., 2004).

7 Collaborative Operations for Personnel Recovery (Co-OPR) Personnel recovery (search and rescue) teams must operate under intense pressure, taking into account not only hard logistics, but also "messy" factors such as the social or political implications of a decision. The “Collaborative Operations for Personnel

28

A. Tate

Recovery” (Co-OPR) project has developed decision-support for sensemaking in such scenarios, seeking to exploit the complementary strengths of human and machine reasoning. Co-OPR integrates the Compendium (Buckingham-Sum et al., 2006) sensemaking-support tool for real time information and argument mapping, with the I-X (Tate et al., 2002) artificial intelligence planning and execution framework to support group activity and collaboration. Both share a common model for dealing with issues, the refinement of options for the activities to be performed, handling constraints and recording other information. The tools span the spectrum with Compendium being very flexible with few constraints on terminology and content, to the knowledge-based reliance on rich domain models and formal conceptual models (ontologies) of I X. In a personnel recovery experimental simulation of a UN peacekeeping operation, with roles played by military planning staff, the Co-OPR tools were judged by external evaluators to have been very effective.

8 Collaborative Advanced Knowledge Technologies e-Response The Collaborative Advanced Knowledge Technologies in the Grid (CoAKTinG) project (Buckingham Shum et al., 2002) used technologies from the UK Advanced Knowledge Technologies programme (Aktors, 2006) to support distributed scientific collaboration in an emergency response situation – in particular in a scenario involving the management of an oil spill. Focusing on the interchange between humans in the scenario, CoAKTinG provided tools to assist scientific collaboration by integrating intelligent meeting spaces, ontologically annotated media streams from on-line meetings, decision rationale and group memory capture, meeting facilitation, planning and coordination support, argumentation, and instant messaging/presence. The focus of AKT as a whole is on the provision of ‘next generation’ knowledge technologies, particularly in the context of the semantic web as both a medium and a target domain for these technologies. New work on the AKT project is focused on a challenge problem dealing with the aftermath of a civil cargo aircraft crashing on a large city – an actual scenario for which there are existing contingency plans in place. It looks at how to use the semantic web to assist in making sense of the situation, both to guide emergency responders and to find appropriate specialized rescue and medical capabilities.

9 OpenKnowledge e-Response OpenKnowledge (OpenKnowledge, 2006) is a European Union project involving Edinburgh, Southampton and the Open Universities in the UK, Amsterdam in The Netherlands, Trento in Italy and Barcelona in Spain. The goal of the project is to provide an open framework for agent interaction and coordination in knowledge-rich environments. It focuses on two challenge problems, one of which is emergency monitoring and management. This domain has been chosen as a testbed because it demands a combination of geographical and geo-presence knowledge alongside active support for collaboration and planning in multi-agent, dynamic situations. The need to harness electronic communication networks in emergency situations has been recognised as a European research priority. Quoting from the EU “Emergency Response Grid” programme (EU, 2006):

The Helpful Environment: Distributed Agents and Services Which Cooperate

29

“In times of crisis – be it a natural disaster, terrorist attack or infrastructure failure – mobile workers need to work together in time-critical and dangerous situations. Real-time access to information and knowledge, powered by Grids, will help save lives. Crises are complex situations, with large numbers and varieties of mobile workers – medical and rescue teams, police, fire fighters and other security personnel – appearing on the spot at short notice. These different teams come from different organisations, and generally have incomplete or even contradictory knowledge of the crisis situation.” For OpenKnowledge the challenge of a test bed in this domain is in the rapidity of formation of emergency coalitions – often very intense and opportunistic “communities of practice” where judging the quality of an answer is critical and dynamic. OpenKnowledge involves the exploration of peer-to-peer services such as: • • • •

Network data sources: Sensor data flows need to be coordinated in the large. These data will be classified locally and will need to be made available to different “experts/peers” on the basis of contextual classification schema. Collaborative services: to support planning, communication and coordination within the expert peers community. Mitigation assessment services: as potential emergency situations are identified, mitigation needs can be determined and prioritized. Preparedness services: supporting those activities that prepare for actual emergencies.

10 FireGrid A broad vision for an emergency response system in the modern built environment is being explored with an integrated and inter-disciplinary approach in FireGrid (Berry et al., 2005; FireGrid, 2006). This is a UK project to address emergency response in the built environment, where sensor grids in large-scale buildings are linked to fasterthan-real-time grid-based simulations, and used to assist fire responders to work with the building’s internal response systems and occupants to form a team to deal with the emergency. FireGrid will integrate several core technologies, extending them where necessary: • • • •

High Performance Computing involving fire models and structural models Wireless sensors in extreme conditions with adaptive routing algorithms, including input validation and filtering Grid computing including sensor-guided computations, mining of data streams for key events and reactive priority-based scheduling Command and Control using knowledge-based planning techniques with user guidance

I-X will act as a “front-end” for the emergency response chief to access the various grid and web services which can be called upon to work with the human responders and those in the affected buildings.

30

A. Tate

11 Helpful Systems and Helpful Organisations The infrastructure and organisations that would be required to make the vision of the helpful environment possible are being put in place. The Galileo European Satellite Navigation System (Galileo, 2006) and its mobile geo-location and emergency response services programme will be another spur to development. Both commercial and freely provided emergency response facilities are being interwoven to ensure active development and support over a long period. Examples of the type of ‘helpful organisation’ that will enact this vision are already starting to emerge. An organisation called the Multinational Planning Augmentation Team (MPAT; Weide, 2006), consisting of 33 nations situated around the Pacific Rim, has been developing shared knowledge and procedures to assist in responses to regional crises. MPAT used computer collaboration aids, and a simple brokering system, during the December 2004 – February 2005 Indian Ocean Tsunami response to help affected countries gain access the specialized capabilities of response organisations more effectively. MPAT is an excellent example of people training and working together to ensure they are ready to respond better to emergencies. It would be a prime beneficiary and exploiter of any future ‘helpful environment’. On a smaller scale the Washington DC area is conducting a programme called Capital Wireless Integrated Network (CapWIN, 2005) led by IBM and the University of Maryland. This programme works with local services to support responses by all agencies to incidents occurring on a single freeway interchange in the DC area. Its aim is to show the potential value of coordinated wireless computing services for integrated and cohesive response across the police, the ambulance service, emergency support teams, the fire brigade, hazardous material units and the military.

12 The Helpful Environment Imagine in the not too distant future that every citizen, vehicle, package in transit and other "active devices" can be treated as a potential sensor or responder. Individuals or vehicles that need help, as well as local, regional, national and international emergency agencies, could look up specialized capabilities or find local assistance through a much more responsive and effective environment. Systems could inter-operate to enable preventative measures to be taken so that those in imminent danger could be forewarned by their own systems, and by the people, vehicles and buildings around them. These would support a diverse range of uses, such as: • • • • • • • • •

Disaster response and evacuation Terrorism incident response Civil accidents Disease control Business continuity Family emergencies Transportation aids Help desks Procedural assistance

A truly "helpful environment" could be created that is accessible by all.

The Helpful Environment: Distributed Agents and Services Which Cooperate

31

Acknowledgments. This article is based on evidence given to US and UK government agencies concerned with responses to emergency situations and with “Grand Challenges” for computer science research. A version is included in the IEEE Intelligent Systems special issue on the Future of AI in 2006. Projects mentioned in this paper have been sponsored by a number of organisations. The University of Edinburgh and research sponsors are authorized to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.

References 1. Aktors (2006) AKT: Advanced Knowledge Technologies.http://aktors.org 2. Allsopp, D.N., Beautement, P., Bradshaw, J.M., Durfee, E.H., Kirton, M., Knoblock, C.A., Suri, N., Tate A., and Thompson, C.W. (2002) Coalition Agents Experiment: Multi-Agent Co-operation in an International Coalition Setting, Special Issue on Knowledge Systems for Coalition Operations (KSCO), IEEE Intelligent Systems, Vol. 17 No. 3 pp. 26-35. May/June 2002. 3. Allsopp, D., Beautement, P., Kirton, M., Tate, A., Bradshaw, J.M., Suri, N. and Burstein, M. (2003) The Coalition Agents Experiment: Network-Enabled Coalition Operations, Special Issue on Network-enabled Capabilities, Journal of Defence Science, Vol. 8, No. 3, pp. 130-141, September 2003. 4. Berry, D., Usmani, A., Terero, J., Tate, A., McLaughlin, S., Potter, S., Trew, A., Baxter, R., Bull, M. and Atkinson, M. (2005) FireGrid: Integrated Emergency Response and Fire Safety Engineering for the Future Built Environment, UK e-Science Programme All Hands Meeting (AHM-2005), 19-22 September 2005, Nottingham, UK. http://www.aiai.ed.ac.uk/project/ix/documents/2005/2005-escience-ahm-firegrid.doc 5. Bradshaw, J M, Dutfield, S, Benoit, P, and Woolley, J D. (1997) KAoS: Toward an industrial-strength open agent architecture. In Software Agents, AAAI Press/MIT Press, Cambridge, Massachusetts, editor J M Bradshaw. Pages 375-418. 6. Buckingham Shum, S., De Roure, D., Eisenstadt, M., Shadbolt, N. and Tate, A. (2002) CoAKTinG: Collaborative Advanced Knowledge Technologies in the Grid, Proceedings of the Second Workshop on Advanced Collaborative Environments, Eleventh IEEE Int. Symposium on High Performance Distributed Computing (HPDC-11), July 24-26, 2002, Edinburgh, Scotland. http://www.aiai.ed.ac.uk/project/ix/documents/2002/2002-wacecoakting.pdf 7. Buckingham Shum, S., Selvin, A., Sierhuis, M., Conklin, J., Haley, C. and Nuseibeh, B. (2006). Hypermedia Support for Argumentation-Based Rationale: 15 Years on from gIBIS and QOC. In: Rationale Management in Software Engineering (Eds.) A.H. Dutoit, R. McCall, I. Mistrik, and B. Paech. Springer-Verlag: Berlin 8. CapWIN (2006) Capital Wireless Integrated Network. http://capwin.org 9. Co-OPR (2006) Coalition Operations for Personnel Recovery. http://www.aiai.ed.ac.uk/ project/co-opr/ 10. CoSAR-TS (2006) Coalition Search and Rescue Task Support. http://www.aiai.ed.ac.uk/ project/cosar-ts/ 11. CoAX (2006) Coalition Agents eXperiment http://www.aiai.ed.ac.uk/project/coax/ 12. EU (2006) European Union Emergency Response Grid www.cordis.lu/ist/grids/ emergencey_response_grid.htm

32

A. Tate

13. FireGrid (2006) FireGrid: The FireGrid Cluster for Next Generation Emergency Response Systems. http://firegrid.org 14. Galileo (2006) Galileo: European Satellite Navigation System. http://europa.eu.int/comm/ dgs/energy_transport/galileo/ 15. I-Rescue (2006) I-Rescue: I-X for Emergency Response and Related Grand Challenges. http://i-rescue.org 16. Kitano, H. and Tadokoro, S. (2001) RoboCup Rescue: A Grand Challenge for Multiagent and Intelligent Systems, Artificial Intelligence Magazine, Spring, 2001, American Association of Artificial Intelligence. 17. MPAT (2006) Multinational Planning Augmentation Team Multinational Forces Standing Operating Procedures (MNF SOP) http://www2.apan-info.net/mpat/ 18. OpenKnowledge (2006) OpenKnowledge.http://openk.org 19. OWL (2004) Ontology Web Language, World Wide Web Consortium. http://www. w3.org/2004/OWL/ 20. OWL-S (2005) Ontology Web Language for Services. http://www.daml.org/services/owl-s/ 21. Rathmell, R A. (1999) A Coalition force scenario ‘Binni gateway to the Golden Bowl of Africa’. In Proceedings of the International Workshop on Knowledge-Based Planning for Coalition Forces, editor A Tate, pages 115-125, Edinburgh, Scotland, May 1999. Available at http://binni.org. 22. Safety Net (2002) Safety.net Grand Challenge Proposal. CRA Conference on "Grand Research Challenges" in Computer Science and Engineering Workshop, June 23-26, 2002, Warrenton, Virginia. http://www.cra.org/Activities/grand.challenges/slides/ubiquitous.pdf 23. Tate, A., Dalton, J., and J. Stader, J. (2002) I-P2- Intelligent Process Panels to Support Coalition Operations. In Proceedings of the Second International Conference on Knowledge Systems for Coalition Operations (KSCO-2002). Toulouse, France, April 2002. 24. Tate, A. (2003) : an Ontology for Mixed-Initiative Synthesis Tasks. In Proceedings of the Workshop on Mixed-Initiative Intelligent Systems (MIIS) at the International Joint Conference on Artificial Intelligence (IJCAI-03), Acapulco, Mexico, August 2003. 25. Tate, A., Dalton, J., Siebra, C., Aitken, S., Bradshaw, J.M. and Uszok, A. (2004) Intelligent Agents for Coalition Search and Rescue Task Support, AAAI-2004 Intelligent Systems Demonstrator, in Proceedings of the Nineteenth National Conference of the American Association of Artificial Intelligence (AAAI-2004), San Jose, CA, USA, July 2004. http://www.aiai.ed.ac.uk/project/ix/documents/2004/2004-aaai-isd-tate-cosarts.pdf 26. Uszok, A., Bradshaw, J.M., Jeffers, R., Johnson, M., Tate, A., Dalton, J. and Aitken, S. (2004) KAoS Policy Management for Semantic Web Services, IEEE Intelligent Systems, pp. 32-41, July/August 2004. 27. Wark, S., Zschorn, A., Perugini, D., Tate, A., Beautement, P., Bradshaw, J.M. and Suri, N. (2003) Dynamic Agent Systems in the CoAX Binni 2002 Experiment, Special Session on Fusion by Distributed Cooperative Agents at the 6th International Conference on Information Fusion (Fusion 2003), Cairns, Australia, July, 2003. 28. Weide, S.A. (2006) Multinational Crisis Response in the Asia-Pacific Region: The Multinational Planning Augmentation Team Model, in "The Liaison", Center of Excellence in Disaster Management & Humanitarian Assistance (COE-DMHA), February 2006. http://www.coe-dmha.org/liaison.htm

Voting in Cooperative Information Agent Scenarios: Use and Abuse Jeﬀrey S. Rosenschein and Ariel D. Procaccia School of Engineering and Computer Science Hebrew University, Jerusalem, Israel {jeff, arielpro}@cs.huji.ac.il

Abstract. Social choice theory can serve as an appropriate foundation upon which to build cooperative information agent applications. There is a rich literature on the subject of voting, with important theoretical results, and builders of automated agents can beneﬁt from this work as they engineer systems that reach group consensus. This paper considers the application of various voting techniques, and examines nuances in their use. In particular, we consider the issue of preference extraction in these systems, with an emphasis on the complexity of manipulating group outcomes. We show that a family of important voting protocols is susceptible to manipulation by coalitions in the average case, when the number of candidates is constant (even though their worst-case manipulations are N P-hard).

1

Introduction

Research on the theory of social choice, and in particular on its computational aspects, has become an important pursuit within computer science (CS). Motivating this work is the belief that social choice theory can have direct implications on the building of systems comprised of multiple automated agents. This paper describes some of that research, so it is a paper about voting and manipulation, but it is also inter alia about how computer science can help scientists and mathematicians see questions in new ways, spurring progress in new theoretical and applied directions. Computer science occupies a unique position with respect to other ﬁelds of scientiﬁc endeavor. Its idiosyncratic nature should be celebrated and strengthened; it helps to make computer science in general, and its subﬁeld multiagent systems (MAS), among the most exciting of scientiﬁc research areas today. Computer Science is, at one and the same time: 1. An independent ﬁeld with its own set of fundamental questions (both theoretical and applied). This distinctive blurring of theoretical and applied research, in both computer science and MAS, can be a great strength. There exist established ﬁelds of Applied Physics and Applied Mathematics, but there is no Applied Computer Science. Fundamental research certainly exists in those parts of CS closest to mathematics, but it is hard to conceive of M. Klusch, M. Rovatsos, and T. Payne (Eds.): CIA 2006, LNAI 4149, pp. 33–50, 2006. c Springer-Verlag Berlin Heidelberg 2006

34

J.S. Rosenschein and A.D. Procaccia

a computer scientist, even one who occupies its most mathematical corners, uttering anything analogous to the quote attributed to the mathematician Leonard Eugene Dickson, “Thank God that number theory is unsullied by any application”.1 It’s the aspiration to be purely theoretical that is absent in computer science. 2. A contributor to other ﬁelds of both technological and conceptual enablers; as the much-quoted New York Times article provocatively put it, “All Science is Computer Science” [18]. However, that article was referring mainly to the use of powerful computing in other ﬁelds, which is certainly not the core of computer science. One could argue that the more important inﬂuence of CS has been in changing how scientists in other ﬁelds view their problems: computation becomes a basic conceptual model, part of the intellectual toolset of other ﬁelds. There have been a number of highly visible examples of this trend, such as in cognitive psychology (e.g., information processing models of memory and attention [11]), attempts to develop computational models of the cell [27], and computational statistical mechanics [17]. The type of work described in this paper has had an inﬂuence on political science and sociology. 3. An avid consumer of the results produced by other ﬁelds. The interdisciplinary nature of computer science is nowhere more evident than in the area of artiﬁcial intelligence (AI), and perhaps nowhere more evident in artiﬁcial intelligence than in its subarea of multiagent systems. MAS researchers for the last 20 years have eagerly derived inspiration from ﬁelds as diverse as biology, physics, sociology, economics, organization theory, and mathematics. This is appropriate (even necessary), and the ﬁeld takes a justiﬁable pride in its interdisciplinary openness. 1.1

Game Theory and Economics in MAS

One of the most exciting trends in computer science (and speciﬁcally in MAS) has been the investigation of game theory and economics as tools for automated systems.2 Beginning with work in the mid-1980s, researchers have turned out a steady drumbeat of results, considering the computational aspects of game theory and economics, and how these ﬁelds can be put to appropriate use in the building of automated agents [23,9,29,24,19,15]. In this paper, we explore the use of preference aggregation in multiagent systems. Preference aggregation has deep roots in economics, but what distinguishes the CS work on this issue is the concern for computational issues: how are results arrived at (e.g., equilibrium points)? What is the complexity of the process? Can complexity be used to guard against unwanted phenomena? Does complexity of computation prevent realistic implementation of a technique? The criteria to be used in evaluating this work (and exploiting its results) needs to take into account the ultimately applied nature of the endeavor. The 1

2

Of course, number theory eventually provided the basis for the cryptography embedded throughout the internet, so it was not quite as unsullied as all that (eventually). We use the terms loosely, to encompass related ﬁelds and subﬁelds such as decision theory, mechanism design, and general equilibrium theory.

Voting in Cooperative Information Agent Scenarios: Use and Abuse

35

idealized models of classic game theory might fall short when used normatively or descriptively with regard to human behavior, but for the most part that might remain a theoretical concern. Application of these models to automated agents quite urgently argued for their adjustment. To take one example, if computing an equilibrium point in a particular interaction is computationally infeasible, what would be the meaning of telling an agent to choose one? Much of what follows ﬁrst appeared in [21,22,20], and we use that material (particularly from the ﬁrst of those papers) freely. We present it as a speciﬁc example of how preference aggregation can be handled in automated systems, and how computational issues come to the forefront. 1.2

The Theory and Practice of Preference Aggregation

In multiagent environments, it may be the case that diﬀerent agents have diverse preferences, and it is therefore important to ﬁnd a way to aggregate agent preferences. Even in situations where the agents are cooperative, there may still be independent motivations, goals, or perspectives that require them to come to a consensus. A general scheme for preference aggregation is voting 3 : the agents reveal their preferences by ranking a set of candidates, and a winner is determined according to a voting protocol. The candidates can be entities of almost any conceivable sort. For instance, Ghosh et al. [12] designed an automated movie recommendation system, in which the conﬂicting preferences a user may have about movies were represented as agents, and movies to be suggested were selected according to a voting scheme (in this example there are multiple winners, as several movies are recommended to the user). The candidates in a virtual election could also be items such as beliefs, joint plans [10], or schedules [14]. In fact, to see the generality of the (automated) voting scenario, consider modern web searching. One of the most massive preference aggregation schemes in existence is Google’s PageRank algorithm, which can be viewed as a vote among indexed web pages on candidates determined by a user-input search string; winners are ranked (Tennenholtz [26] considers the axiomatic foundations of ranking systems such as this). Things are made more complicated by the fact that in many automated settings (as in non-virtual environments) the agents are self-interested, or at the very least bring diﬀerent knowledge/perspectives to the interaction. Such an agent may reveal its preferences untruthfully, if it believes this would make the ﬁnal outcome of the elections more favorable for it. In fact, well-meaning agents may even lie in an attempt to improve social welfare [20].4 In any case, whether the agents are acting out of noble or ignoble motives, there may be an undesirable social outcome. Strategic behavior in voting has been of particular interest to AI researchers [3,5,8,21]. This problem is provably acute: it is known [13,25] that, for elections with three or more candidates, in any voting protocol that 3

4

We use the term in its intuitive sense here, but in the social choice literature, “preference aggregation” and “voting” are basically synonymous. Though if several do it in parallel there may be unintended negative consequences.

36

J.S. Rosenschein and A.D. Procaccia

is non-dictatorial,5 there are elections where an agent is better oﬀ by voting untruthfully. Of course, in some systems, particularly centrally-designed systems, strategic behavior can be eﬀectively banned by ﬁat. We would argue this covers a distinct minority of multiagent systems. 1.3

Nuances

Diﬀerent voting protocols will often have diﬀerent outcomes, as well as diﬀerent properties. Some protocols may be hard to manipulate; others may skew the preferences of voters in particular ways. Many of these phenomena are wellknown in social choice theory, such as the eﬀects of run-oﬀ voting on who wins an election (a good heuristic: bring your candidate up for a vote as late as possible in the process). Consider as another example one that comes from a paper in this collection [20]. There we discuss the concepts of distortion and misrepresentation, two (related, but distinct) measures of how well (or badly) a given candidate represents the desires of a voter. Quoting from [20]: [T]he misrepresentation of a social choice function. . . can be easily reformulated as distortion. In fact, similar results can be obtained, but the latter formulation favors candidates that are ranked last by few voters, whereas the former formulation rewards candidates that are placed ﬁrst by many voters. So depending on whether distortion or misrepresentation is used, we may be developing a technique that prefers candidates with many ﬁrst place votes over one that prefers candidates with few last place votes. The choice is in the hands of the designer, and one or the other may be more natural for some speciﬁc domain. Continuing from [20]: Consider the meeting scheduling problem discussed in [14]: scheduling agents schedule meetings on behalf of their associated users, based on given user preferences; a winning schedule is decided in an election. Say three possible schedules are being voted on. These schedules, being fair, conﬂict with at most two of the requirements speciﬁed by any user. . . In this case, having no conﬂicts at all is vastly superior to having at least one conﬂict, as even one conﬂict may prevent a user from attending a meeting. As noted above, this issue is taken into account in the calculation of misrepresentation — emphasis is placed on candidates that were often ranked ﬁrst. This type of consideration runs throughout our choices of preference aggregation techniques. The system put into place may have major ramiﬁcations on the outcomes. In addition (and this is the main concern of the rest of the paper), 5

In a dictatorial protocol, there is an agent that dictates the outcome regardless of the others’ choices.

Voting in Cooperative Information Agent Scenarios: Use and Abuse

37

the resistance of the system to strategic behavior may inﬂuence whether agents truthfully reveal their preferences, and whether a socially desirable candidate is elected. 1.4

Complexity to the Rescue

Fortunately, it is reasonable to make the assumption that automated agents are computationally bounded. Therefore, although in principle an agent may be able to manipulate an election, the computation required may be infeasible. This has motivated researchers to study the computational complexity of manipulating voting protocols. It has long been known [2] that there are voting protocols that are N P-hard to manipulate by a single voter. Recent results by Conitzer and Sandholm [6,4] show that some manipulations of common voting protocols are N P-hard, even for a small number of candidates. Moreover, in [7], it is shown that adding a pre-round to some voting protocols can make manipulations hard (even PSPACE-hard in some cases). Elkind and Lipmaa [8] show that the notion of pre-round, together with one-way functions, can be used to construct protocols that are hard to manipulate even by a large minority fraction of the voters. In computer science, the notion of hardness is usually considered in the sense of worst-case complexity. Not surprisingly, most results on the complexity of manipulation use N P-hardness as the complexity measure. However, it may still be the case that most instances of the problem are easy to manipulate. A relatively little-known theory of average case complexity exists [28]; that theory introduces the concept of distributional problems, and deﬁnes what a reduction between distributional problems is. It is also known that average-case complete problems exist (albeit artiﬁcial ones, such as a distributional version of the halting problem). Sadly, it is very diﬃcult to show that a certain problem is average-case complete, and such results are known only for a handful of problems. Additionally, the goal of the existing theory is to deﬁne when a problem is hard in the averagecase; it does not provide criteria for deciding when a problem is easy. A step towards showing that a manipulation is easy on average was made in [8]. It involves an analysis of the plurality protocol with a pre-round, but focuses on a very speciﬁc distribution, which does not satisfy some basic desiderata as to what properties an “interesting” distribution should have. In this paper, we engage in a novel average-case analysis, based on criteria we propose. Coming up with an “interesting” distribution of problem instances with respect to which the average-case complexity is computed is a diﬃcult task, and the solution may be controversial. We analyze problems whose instances are distributed with respect to a junta distribution. Such a distribution must satisfy several conditions, which (arguably) guarantee that it focuses on instances that are harder to manipulate. We consider a protocol to be susceptible to manipulation when there is a polynomial time algorithm that can usually manipulate it: the probability of failure (when the instances are distributed according to a junta distribution) must be inverse-polynomial. Such an algorithm is known as a heuristic polynomial time algorithm.

38

J.S. Rosenschein and A.D. Procaccia

We use these new methods to show our main result: an important family of protocols, called scoring protocols, is susceptible to coalitional manipulation when the number of candidates is constant.6 Speciﬁcally, we contemplate sensitive scoring protocols, which include such well-known protocols as Borda and Veto. To accomplish this task, we deﬁne a natural distribution μ∗ over the instances of a well-deﬁned coalitional manipulation problem, and show that this is a junta distribution. Furthermore, we present the manipulation algorithm Greedy, and show that it usually succeeds with respect to μ∗ . We also show that all protocols are susceptible to a certain setting of manipulation, where the manipulator is unsure about the others’ votes. This result depends upon a basic conjecture regarding junta distributions, but also has implications that transcend our speciﬁc deﬁnition of these distributions. In Section 2, we outline some important voting protocols, and properly deﬁne the manipulation problems we shall discuss. In Section 3, we formally introduce the tools for our average case analysis: junta distributions, heuristic polynomial time, and susceptibility to manipulations. In Section 4 we present our main result: sensitive scoring protocols are susceptible to coalitional manipulation with few candidates. In Section 5, we discuss the case when a single manipulator is unsure about the other voters’ votes. Finally, in Section 6, we present conclusions and directions for future research.

2

Preliminaries

We ﬁrst describe some common voting protocols and formally deﬁne the manipulation problems with which we shall deal. Next, we introduce a useful lemma from probability theory. 2.1

Elections and Manipulations

An election consists of a set C of m candidates, and a set V of n voters, who provide a total order on the candidates. An election also includes a winner determination function from the set of all possible combinations of votes to C. We note that throughout this paper, m = O(1), so the complexity results are in terms of n. Diﬀerent voting protocols are distinguished by their winner determination functions. The protocols we shall discuss are: – Scoring protocols: A scoring protocol is deﬁned by vector α =α1 , α2 , . . . , αm , such that α1 ≥ α2 ≥ . . . ≥ αm and αi ∈ N ∪ {0}. A candidate receives αi points for each voter which ranks it in the i’th place. Examples of scoring protocols are: • Plurality: α = 1, 0, . . . , 0, 0. • Veto: α = 1, 1, . . . , 1, 0. • Borda: α = m − 1, m − 2, . . . , 1, 0. 6

Proofs can be found in [21].

Voting in Cooperative Information Agent Scenarios: Use and Abuse

39

– Copeland: For each possible pair of candidates, simulate an election; a candidate wins such a pairwise election if more voters prefer it over the opponent. A candidate gets 1 point for each pairwise election it wins, and −1 for each pairwise election it loses. – Maximin: A candidate’s score in a pairwise election is the number of voters that prefer it over the opponent. The winner is the candidate whose minimum score over all pairwise elections is highest. – Single Transferable Vote (STV): The election proceeds in rounds. In each round, the candidate’s score is the number of voters that rank it highest among the remaining candidates; the candidate with the lowest score is eliminated. Remark 1. We assume that tie-breaking is always adversarial to the manipulator.7 In the case of weighted votes, a voter with weight k ∈ N is naturally regarded as k voters who vote unanimously. In this paper, we consider weights in [0, 1]. This is equivalent, since any set of integer weights in the range 1, . . . , polyn can be scaled down to weights in the segment [0, 1] with O(logn) bits of precision. The main results of the paper focus on scoring protocols. We shall require the following deﬁnition: Deﬁnition 1. Let P be a scoring protocol with parameters α =α1 , α2 , . . . , αm . We say that P is sensitive iﬀ α1 ≥ α2 ≥ . . . ≥ αm−1 > αm = 0 (notice the strict inequality on the right). In particular, Borda and Veto are sensitive scoring protocols. Remark 2. Generally, from any scoring protocol with αm−1 > αm , an equivalent sensitive scoring protocol can be obtained by subtracting αm on a coordinateby-coordinate basis from the vector α. Moreover, observe that if a protocol is a scoring protocol but is not sensitive, and αm = 0, then αm−1 = 0. In this case, for three candidates it is equivalent to the plurality protocol, for which most manipulations are tractable even in the worst-case. Therefore, it is suﬃcient to restrict our results to sensitive scoring protocols. We next consider some types of manipulations, state the appropriate complexity results, and introduce some notations. Remark 3. We discuss the constructive cases, where the goal is trying to make a candidate win, as opposed to destructive manipulation, where the goal is to make a candidate lose. Constructive manipulations are always at least as hard (in the worst-case sense) as their destructive counterparts, and in some cases strictly harder (if one is able to determine whether p can be made to win, one can also ask whether any of the other m − 1 candidates can be made to win, thus making p lose). 7

This is a standard assumption, also made, for example, in [6,4]. It does, indeed, make it more straightforward to prove certain results.

40

J.S. Rosenschein and A.D. Procaccia

Deﬁnition 2. In the Individual-Manipulation problem, we are given all the other votes, and a preferred candidate p. We are asked whether there is a way for the manipulator to cast its vote so that p wins. Bartholdi and Orlin [2] show that IM is N P-complete in Single Transferable Vote, provided the number of candidates is unbounded. However, the problem is in P for most voting schemes, and hence will not be studied here. Deﬁnition 3. In the Coalitional-Weighted-Manipulation (CWM) problem, we are given a set of weighted votes S, the weights of a set of votes T which have not been cast, and a preferred candidate p. We are asked whether there is a way to cast the votes in T so that p wins the election. We know [6,4] that CWM is N P-complete in Borda, Veto and Single Transferable Vote, even with 3 candidates, and in Maximin and Copeland with at least 4 candidates. The CWM version that we shall analyze, which is speciﬁcally tailored for scoring protocols, is a slightly modiﬁed version whose analysis is more straightforward: Deﬁnition 4. In the Scoring-Coalitional-Weighted-Manipulation (SCWM) problem, we are given an initial score S[c] for each candidate c, the weights of a set of votes T which have not been cast, and a preferred candidate p. We are asked whether there is a way to cast the votes in T so that p wins the election. S[c] can be interpreted as c’s total score from the votes in S. However, we do not require that there exist a combination of votes that actually induces S[c] for all c. Deﬁnition 5. In the Uncertain-Votes-Weighted-Evaluation (UVWE) problem, we are given a weight for each voter, a distribution over all the votes, a candidate p, and a number r ∈ [0, 1]. We are asked whether the probability of p winning is greater than r. Deﬁnition 6. In the Uncertain-Votes-Weighted-Manipulation (UVWM) problem, we are given a single manipulative voter with a weight, weights for all other voters, a distribution over all the others’ votes, a candidate p, and a number r, where r ∈ [0, 1]. We are asked whether the manipulator can cast its vote so that p wins with probability greater than r. If CWM is N P-hard in a protocol, then UVWE and UVWM are also N P-hard in it [6]. These problems will be studied in Section 5. We make the assumption that the given distributions over the others’ votes can be sampled in polynomial time. 2.2

Chernoﬀ ’s Bounds

The following lemma will be of much use later on. Informally, it states that the average of independent identically distributed (i.i.d.) random variables is almost always close to the expectation.

Voting in Cooperative Information Agent Scenarios: Use and Abuse

41

Lemma 1 (Chernoﬀ ’s Bounds). Let X1 , . . . , Xt be i.i.d. random variables such that a ≤ Xi ≤ b and E[Xi ] = μ. Then for any > 0, it holds that: – Pr[ 1t – Pr[ 1t

3

t

i=1 Xi ≥ μ + ] ≤ e

t

i=1

Xi ≤ μ − ] ≤ e

2

−2t (b−a) 2 2

−2t (b−a) 2

Junta Distributions and Susceptible Mechanisms

In this section we lay the mathematical foundations required for an average-case analysis of the complexity of manipulations. All of the deﬁnitions are as general as possible; they can be applied to the manipulation of any mechanism, not merely to the manipulation of voting protocols. We describe a distribution over the instances of a problem as a collection of distributions μ1 , . . . , μn , . . ., where μn is a distribution over the instances x such that |x| = n. We wish to analyze problems whose instances are distributed with respect to a distribution which focuses on hard-to-manipulate instances. Ideally, we would like to insure that if one manages to produce an algorithm which can usually manipulate instances according to this distinguished “diﬃcult” distribution, the algorithm would also usually succeed when the instances are distributed with respect to most other reasonable distributions. Deﬁnition 7. Let μ = {μn }n∈N be a distribution over the possible instances of an N P-hard manipulation problem M . μ is a junta distribution if and only if μ has the following properties: 1. Hardness: The restriction of M to μ is the manipulation problem whose possible instances are only: {x : |x| = n ∧ μn (x) > 0}. n∈N

Deciding this restricted problem is still N P-hard. 2. Balance: There exist a constant c > 1 and N ∈ N such that for all n ≥ N : 1 1 ≤ Prx∼μn [M (x) = 1] ≤ 1 − . c c 3. Dichotomy: for all n and instances x such that |x| = n: μn (x) ≥ 2−polyn ∨ μn (x) = 0. If M is a voting manipulation problem, we also require the following property: 4. Symmetry: Let v be a voter whose vote is given, let c1 , c2 = p be two candidates, and let i ∈ {1, . . . , m}. The probability that v ranks c1 in the i’th place is the same as the probability that v ranks c2 in the i’th place.

42

J.S. Rosenschein and A.D. Procaccia

If M is a coalitional manipulation problem, we also require the following property: 5. Reﬁnement: Let x be an instance such that |x| = n and μn (x) > 0; if all colluders voted identically, then p would not be elected. The name “junta distribution” comes from the idea that in such a distribution, relatively few “powerful” and diﬃcult instances represent all the other problem instances. Alternatively, our intent is to have a few problematic distributions (the family of junta distributions) convincingly represent all other distributions with respect to the average-case analysis. The ﬁrst three properties are basic, and are relevant to problems of manipulating any mechanism. The deﬁnition is modular, and additional properties may be added on top of the basic three, in case one wishes to analyze a mechanism which is not a voting protocol. The exact choice of properties is of extreme importance (and, as we mentioned above, may be arguable). We shall brieﬂy explain our choices. Hardness is meant to insure that the junta distribution contains hard instances. Balance guarantees that a trivial algorithm which always accepts (or always rejects) has a signiﬁcant chance of failure. The dichotomy property helps in preventing situations where the distribution gives a (positive but) negligible probability to all the hard instances, and a high probability to several easy instances. We now examine the properties that are speciﬁc to manipulation problems. The necessity of symmetry is best explained by an example. Consider CWM in STV with m ≥ 3. One could design a distribution where p wins if and only if a distinguished candidate loses the ﬁrst round. Such a distribution could be tailored to satisfy the other conditions, but misses many of the hard instances. In the context of SCWM, we interpret symmetry in the following way: for every two candidates c1 , c2 = p and y ∈ R, Pr [S[c1 ] = y] = Pr [S[c2 ] = y].

x∼μn

x∼μn

Reﬁnement is less important than the other four properties, but seems to help in concentrating the probability on hard instances. Observe that reﬁnement is only relevant to coalitional manipulation; we believe that in the analysis of individual voting manipulation problems, the ﬁrst four properties are suﬃcient. Deﬁnition 8. [28] A distributional problem is a pair L, μ where L is a decision problem and μ is a distribution over the set {0, 1}∗ of possible inputs. Informally, an algorithm is a heuristic polynomial time algorithm for a distributional problem if it runs in polynomial time, and fails only on a small fraction of the inputs. We now give a formal deﬁnition; this deﬁnition is inspired by [28] (there the same name is used for a somewhat diﬀerent deﬁnition). Deﬁnition 9. Let M be a manipulation problem and let M, μ be a distributional problem.

Voting in Cooperative Information Agent Scenarios: Use and Abuse

43

1. An algorithm A is a deterministic heuristic polynomial time algorithm for the distributional manipulation problem M, μ if A always runs in polynomial time, and there exists a polynomial p and N ∈ N such that for all n ≥ N : Pr [A(x) = M (x)] <

x∼μn

1 . p(n)

(1)

2. Let A be a probabilistic algorithm, which uses a random string s. A is a probabilistic heuristic polynomial time algorithm for the distributional manipulation problem M, μ if A always runs in polynomial time, and there exists a polynomial p and N ∈ N such that for all n ≥ N : Pr [A(x) = M (x)] <

x∼μn ,s

1 . p(n)

(2)

Probabilistic algorithms have two potential sources of failure: an unfortunate choice of input, or an unfortunate choice of random string s. The success or failure of deterministic algorithms depends only on the choice of input. We now combine all the deﬁnitions introduced in this section in an attempt to establish when a mechanism is susceptible to manipulation in the average case. The following deﬁnition abuses notation a bit: M is both used to refer to the manipulation itself, and the corresponding decision problem. Deﬁnition 10. We say that a mechanism is susceptible to a manipulation M if there exists a junta distribution μ, such that there exists a deterministic/probabilistic heuristic polynomial time algorithm for M, μ.

4

Susceptibility to SCWM

Recall [6,4] that in Borda and Veto, CWM is N P-hard, even with 3 candidates. Since Borda and Veto are examples of sensitive scoring protocols, we would like to know how resistant this family of protocols really is with respect to coalitional manipulation. In this section we use the methods from the previous section to present our main result: Theorem 1. Let P be a sensitive scoring protocol. Then P , with candidates C = {p, c1 , . . . , cm }, m = O(1), is susceptible to SCWM. Intuitively, the instances of CWM (or SCWM) which are hard are those that require a very speciﬁc partitioning of the voters in T to subsets, where each subset votes unanimously. These instances are rare in any reasonable distribution; this insight will ultimately yield the theorem. The following proposition generalizes Theorem 1 of [6] and Theorem 2 of [4], and justiﬁes our focus on the family of sensitive scoring protocols. A stronger version of Proposition 1 has been independently proven in [16]. Proposition 1. Let P be a sensitive scoring protocol. Then CWM in P is N Phard, even with 3 candidates.

44

J.S. Rosenschein and A.D. Procaccia

Deﬁnition 11. In the Partition problem, we are given a set of integers {ki }i∈[t] , summing to 2K, and are asked whether a subset of these integers sum to K. It is well-known that Partition is N P-complete. Proof (of Proposition 1). All proofs in the paper are omitted, but can be seen in [21]. Since an instance of CWM can be translated to an instance of SCWM in the obvious way, we have: Corollary 1. Let P be a sensitive scoring protocol. It holds that SCWM in P is N P-hard, even with 3 candidates. 4.1

A Junta Distribution

Let w(v) denote the weight of voter v, and let W denote the total weight of the votes in T ; P is a sensitive scoring protocol. We denote |T | = n: the size of T is the size of the instance. Consider a distribution μ∗ = {μ∗n }n∈N over the instances of CWM in P , with m + 1 candidates p, c1 , . . . , cm , where each μ∗n is induced by the following sampling algorithm: 1. ∀v ∈ T : Randomly and independently choose w(v) ∈ [0, 1] (up to O(logn) bits of precision). 2. ∀i∈ {1, . . . , m}: Randomly and independently choose S[ci ]∈ [(α1−α2 )W, α1 W ] (up to O(logn) bits of precision). We assume that S[p] = 0, i.e., all voters in S rank p last. This assumption is not a restriction. If it holds for a candidate c that S[c] ≤ S[p], then candidate c will surely lose, since the colluders all rank p ﬁrst. Therefore, if S[p] > 0, we may simply normalize the scores by subtracting S[p] from the scores of all candidates. This is equivalent to our assumption. Remark 4. We believe that μ∗ is the most natural distribution with respect to which coalitional manipulation in scoring protocols should be studied. Even if one disagrees with the exact deﬁnition of junta distribution, μ∗ should satisfy many reasonable conditions one could produce. We shall, of course, (presently) show that the distribution possesses the properties of a junta distribution. Proposition 2. Let P be a sensitive scoring protocol. Then μ∗ is a junta distribution for SCWM in P with C = {p, c1 , . . . , cm }, and m = O(1). 4.2

A Heuristic Polynomial Time Algorithm

We now present our algorithm Greedy for SCWM, given as Algorithm 1. w denotes the vector of the weights of voters in T = {t1 , . . . , tn }.

Voting in Cooperative Information Agent Scenarios: Use and Abuse

45

Algorithm 1. Decides SCWM 1: procedure Greedy(S, w, p) 2: for all c ∈ C do 3: S0 [c] ← S[c] 4: end for 5: for i = 1 to n do 6: Let j1 , j2 , . . . , jm s.t. ∀l, Si−1 [cjl−1 ] ≤ Si−1 [cjl ] 7: Voter ti votes p cj1 cj2 . . . cjm 8: for l = 1 to m do 9: Si [cjl ] ← Si−1 [cjl ] + w(ti )αl+1 10: end for 11: Si [p] ← Si−1 [p] + w(ti )α1 12: end for 13: if argmaxc∈C Sn [c] = {p} then 14: return true 15: else 16: return false 17: end if 18: end procedure

Initialization

All voters in T

Update score

p wins

The voters in T , according to some order, each rank p ﬁrst, and the rest of the candidates by their current score: the candidate with the lowest current score is ranked highest. Greedy accepts if and only if p wins this election. This algorithm, designed speciﬁcally for scoring protocols, is a realization of an abstract greedy algorithm: at each stage, voter ti ranks the undesirable candidates in an order that minimizes the highest score that any undesirable candidate obtains after the current vote. If there is a tie between several permutations, the voter chooses the option such that the second highest score is as low as possible, etc. In any case, every colluder always ranks p ﬁrst. Remark 5. This abstract scheme might also be appropriate for protocols such as Maximin and Copeland. Similarly to scoring protocols, in these two protocols the colluders are always better oﬀ by ranking p ﬁrst. In addition, the abstract greedy algorithm can be applied to Maximin and Copeland since the result of an election is based on the score each candidate has (unlike STV, for example). In the following lemmas, a stage in the execution of the algorithm is an iteration of the for loop. Lemma 2. If there exists a stage i0 during the execution of Greedy, and two candidates a, b = p, such that |Si0 [a] − Si0 [b]| ≤ α2 , then for all i ≥ i0 it holds that |Si [a] − Si [b]| ≤ α2 .

(3)

46

J.S. Rosenschein and A.D. Procaccia

Lemma 3. Let p = a, b ∈ C, and suppose that there exists a stage i0 such that Si0 [a] ≥ Si0 [b], and a stage i1 ≥ i0 such that Si1 [b] ≥ Si1 [a]. Then for all i ≥ i1 it holds that |Si [a] − Si [b]| ≤ α2 . Lemma 4. Let P be a sensitive scoring protocol, and assume Greedy errs on an instance of SCWM in P which has a successful manipulation. Then there is d ∈ {2, 3, . . . , m}, and a subset of candidates D = {cj1 , . . . , cjd }, such that: d

(α1 W − S[cji ]) −

i=1

≤

d

d−1

d

i=1

i=1

(i · α2 ) ≤ W

αm+2−i (4)

(α1 W − S[cji ]).

i=1

Lemma 5. Let M be SCWM in a sensitive scoring protocol P with C = {p, c1 , . . . , cm }, m=O(1). Then Greedy is a deterministic heuristic polynomial time algorithm for M, μ∗ . Clearly, Theorem 1 directly follows.

5

Susceptibility to UVWM

In this section we shall show: Theorem 2. Let P be a voting protocol such that there exists a junta distribution μP over the instances of UVWM in P , with the following property: r is uniformly distributed in [0, 1]. Then P , with candidates C = {p, c1 , . . . , cm }, m = O(1), is susceptible to UVWM. The existence of a junta distribution with r uniformly distributed is a very weak requirement (it is even quite natural to have r uniformly distributed). In fact, the following claim is very likely to be true: Conjecture 1. Let P be a voting protocol. Then there exists a junta distribution μP over the instances of UVWM in P , with r uniformly distributed in [0, 1]. If this conjecture is indeed true, we have that all voting protocols are susceptible to UVWM. If for some reason the conjecture is not true with respect to our deﬁnition of junta distributions, then perhaps the deﬁnition is too restrictive and should be modiﬁed accordingly. To prove Theorem 2, we require a procedure named Sample, which decides UVWE. Sample samples the given distribution on the votes n3 times, and calculates the winner of the election each time. If p won more than an r-fraction of the elections then the procedure accepts, otherwise it rejects. We omit the details of the procedure.

Voting in Cooperative Information Agent Scenarios: Use and Abuse

47

Algorithm 2. Decides UVWM 1: procedure Sample-and-Manipulate(w, ν, p, r) 2: for all permutations of the m + 1 candidates do 3: π ← next permutation 4: ν ∗ ← the manipulator votes π 5: others’ votes are always distributed w.r.t. ν 6: if Sample(w, ν ∗ , p, r) then 7: return true 8: end if 9: end for 10: return false 11: end procedure

Lemma 6. Let P be a voting protocol, and E be UVWE in P with C ={p, c1 , . . . , cm }. Furthermore, let μ be a distribution over the instances of E, with r uniformly distributed in [0, 1]. Then there exists N such that for all n ≥ N : Pr [Sample(x) = E(x)] ≤

x∼μn

1 . polyn

We now present an algorithm, Sample-and-Manipulate that decides UVWM; it is given as Algorithm 2. Here, w denotes the weights of all voters including the manipulator, and ν is the given distribution over the others’ votes. Given an instance of UVWM, Sample-and-Manipulate generates (m + 1)! instances of the UVWE problem, one for each of the manipulator’s possible votes, and executes Sample on each instance. Sample-and-Manipulate accepts if and only if Sample accepts one of the instances. Lemma 7. Let P be a voting protocol, and M be UVWM in P with C = {p, c1 , . . . , cm }, m = O(1). Furthermore, let μ be a distribution over the instances of UVWM, with r uniformly distributed in [0, 1]. It holds that Sample-andManipulate is a probabilistic heuristic polynomial time algorithm for M, μ.

6

Future Research

The issue of resistance of mechanisms to manipulation is important, particularly in the context of voting protocols. Most results on this issue use N P-hardness as the complexity measure. One of this paper’s main contributions has been introducing tools that can be utilized in showing that manipulating mechanisms is easy in the average case. We were concerned with the likely case of coalitional manipulation, and showed that sensitive scoring protocols are susceptible to such manipulation when the number of candidates is constant. These results suggest that scoring protocols cannot be safely employed. More importantly, this paper should be seen as a starting point for studying the average case complexity of other types of manipulations, in other protocols. In addition, the deﬁnitions in Section 3 are deliberately general, and can be applied to

48

J.S. Rosenschein and A.D. Procaccia

manipulations of mechanisms that are not voting mechanisms. One such mechanism of which we are aware, whose manipulation is N P-hard, is presented in [1]. There is still room for debate as to the exact deﬁnition of a junta distribution, especially if Conjecture 1 turns out to be false. It may also be the case that there are “unconvincing” distributions that satisfy all of the (current) conditions of a junta distribution. It might prove especially fruitful to show that a heuristic polynomial time algorithm with respect to a junta distribution also has the same property with respect to some easy distributions, such as the uniform distribution. An issue of great importance is coming up with natural criteria to decide when a manipulation problem is hard in the average-case. The traditional deﬁnition of average-case completeness is very diﬃcult to work with in general; is there a satisfying deﬁnition that applies speciﬁcally to the case of manipulations? Once the subject is more fully understood, this understanding can be used to design mechanisms that are hard to manipulate in the average-case.

Acknowledgment This work was partially supported by grant #039-7582 from the Israel Science Foundation.

References 1. Yoram Bachrach and Jeﬀrey S. Rosenschein. Achieving allocatively-eﬃcient and strongly budget-balanced mechanisms in the network ﬂow domain for boundedrational agents. In The Nineteenth International Joint Conference on Artiﬁcial Intelligence, pages 1653–1654, Edinburgh, Scotland, August 2005. Full version published in The Seventh International Workshop on Agent-Mediated Electronic Commerce: Designing Mechanisms and Systems (AMEC 2005), Utrecht, The Netherlands, July 2005. 2. J. Bartholdi and J. Orlin. Single transferable vote resists strategic voting. Social Choice and Welfare, 8(4):341–354, 1991. 3. J. Bartholdi, C. A. Tovey, and M. A. Trick. How hard is it to control an election. Mathematical and Computer Modelling, 16:27–40, 1992. 4. V. Conitzer, J. Lang, and T. Sandholm. How many candidates are needed to make elections hard to manipulate? In Proceedings of the International Conference on Theoretical Aspects of Reasoning about Knowledge, pages 201–214, Bloomington, Indiana, 2003. 5. V. Conitzer and T. Sandholm. Complexity of manipulating elections with few candidates. In Proceedings of the National Conference on Artiﬁcial Intelligence, pages 314–319, Edmonton, Canada, July 2002. 6. V. Conitzer and T. Sandholm. Complexity of manipulating elections with few candidates. In Proceedings of the National Conference on Artiﬁcial Intelligence, pages 314–319, Edmonton, Canada, July 2002. 7. V. Conitzer and T. Sandholm. Universal voting protocol tweaks to make manipulation hard. In Proceedings of the International Joint Conference on Artiﬁcial Intelligence, pages 781–788, Acapulco, Mexico, August 2003.

Voting in Cooperative Information Agent Scenarios: Use and Abuse

49

8. E. Elkind and H. Lipmaa. Small coalitions cannot manipulate voting. In International Conference on Financial Cryptography, Lecture Notes in Computer Science. Springer-Verlag, Roseau, The Commonwealth of Dominica, 2005. 9. Eithan Ephrati and Jeﬀrey S. Rosenschein. The Clarke Tax as a consensus mechanism among automated agents. In Proceedings of the Ninth National Conference on Artiﬁcial Intelligence, pages 173–178, Anaheim, California, July 1991. 10. Eithan Ephrati and Jeﬀrey S. Rosenschein. A heuristic technique for multiagent planning. Annals of Mathematics and Artiﬁcial Intelligence, 20:13–67, Spring 1997. 11. Robert M. Gagn´e and Karen L. Medsker. The Conditions of Learning Training Applications. Harcourt Brace & Company, 1996. 12. S. Ghosh, M. Mundhe, K. Hernandez, and S. Sen. Voting for movies: the anatomy of a recommender system. In Proceedings of the Third Annual Conference on Autonomous Agents, pages 434–435, 1999. 13. A. Gibbard. Manipulation of voting schemes. Econometrica, 41:587–602, 1973. 14. T. Haynes, S. Sen, N. Arora, and R. Nadella. An automated meeting scheduling system that utilizes user preferences. In Proceedings of the First International Conference on Autonomous Agents, pages 308–315, 1997. 15. E. Hemaspaandra, L. Hemaspaandra, and J. Rothe. Anyone but him: The complexity of precluding an alternative. In Proceedings of the 20th National Conference on Artiﬁcial Intelligence, Pittsburgh, July 2005. 16. E. Hemaspaandra and L. A. Hemaspaandra. Dichotomy for voting systems. University of Rochester Department of Computer Science Technical Report 861, 2005. 17. William G. Hoover. Computational Statistical Mechanics. Elsevier, 1991. 18. George Johnson. All science is computer science, 25 March 2001. The New York Times, Week in Review, pages 1, 5. 19. Noam Nisan and Amir Ronen. Algorithmic mechanism design. Games and Economic Behavior, 35:166–196, 2001. 20. Ariel D. Procaccia and Jeﬀrey S. Rosenschein. The distortion of cardinal preferences in voting. In The Tenth International Workshop on Cooperative Information Agents (CIA 2006), Edinburgh, September 2006. 21. Ariel D. Procaccia and Jeﬀrey S. Rosenschein. Junta distributions and the averagecase complexity of manipulating elections. In The Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, pages 497–504, Hakodate, Japan, May 2006. 22. Ariel D. Procaccia, Jeﬀrey S. Rosenschein, and Aviv Zohar. Multi-winner elections: Complexity of manipulation, control and winner-determination. In The Eighth International Workshop on Agent-Mediated Electronic Commerce (AMEC 2006), pages 15–28, Hakodate, Japan, May 2006. 23. Jeﬀrey S. Rosenschein and Michael R. Genesereth. Deals among rational agents. In Proceedings of the Ninth International Joint Conference on Artiﬁcial Intelligence, pages 91–99, Los Angeles, California, August 1985. 24. T. Sandholm and V. Lesser. Issues in automated negotiation and electronic commerce: Extending the contract net framework. In Proceedings of the First International Conference on Multiagent Systems (ICMAS-95), pages 328–335, San Francisco, 1995. 25. M. Satterthwaite. Strategy-proofness and Arrow’s conditions: Existence and correspondence theorems for voting procedures and social welfare functions. Journal of Economic Theory, 10:187–217, 1975. 26. Moshe Tennenholtz and Alon Altman. On the axiomatic foundations of ranking systems. In Proceedings of the Nineteenth International Joint Conference on Artiﬁcial Intelligence (IJCAI’05), pages 917–922, Edinburgh, August 2005.

50

J.S. Rosenschein and A.D. Procaccia

27. Jeﬀrey D. Thomas, Taesik Lee, and Nam P. Suh. A function-based framework for understanding biological systems. Annual Review of Biophysics and Biomolecular Structure, 33:75–93, 2004. 28. L. Trevisan. Lecture notes on computational complexity. Available from http:// www.cs.berkeley.edu/˜luca/notes/complexitynotes02.pdf, 2002. Lecture 12. 29. Michael P. Wellman. The economic approach to artiﬁcial intelligence. ACM Computing Surveys, 27:360–362, 1995.

Agents for Information-Rich Environments John Debenham and Simeon Simoﬀ University of Technology, Sydney, Australia {debenham, simeon}@it.uts.edu.au

Abstract. Information-rich environments, such as electronic markets, or even more generally the World Wide Web, require agents that can assimilate and use real-time information ﬂows wisely. A new breed of “information-based” agents aim to meet this requirement. They are founded on concepts from information theory, and are designed to operate with information ﬂows of varying and questionable integrity. These agents are part of a larger project that aims to make informed automated trading, in applications such as eProcurement, a reality.

1

Introduction

Electronic trading environments are awash with information, including information drawn from general resources such as the World Wide Web using smart retrieval technology [1]. Powerful agent architectures that are capable of ﬂexible, autonomous action are well known [2]. However there has been little work on architectures for intelligent agents that are designed the survive and thrive in these information rich environments. This is the question addressed here. This paper describes a data mining system and an agent architecture that have been designed to operate in tandem. These are two components in our e-Market Framework that is available on the World Wide Web1 . This framework aims to make informed automated trading a reality, and develops further the “Curious Negotiator” framework [3]. This work does not address all of the issues in automated trading. For example, the work relies on developments in: XML and semantic web, secure data exchange, value chain management and ﬁnancial services. Further the design of marketplaces is not described here. We are presently constructing a “virtual institution” system1 in a collaborative research project with “Institut d’Investigacio en Intel.ligencia Artiﬁcial2 ”, Spanish Scientiﬁc Research Council, UAB, Barcelona, Spain. The data mining system is described in Sec. 2. The associated intelligent agents, designed speciﬁcally to handle real-time information ﬂows, are described in Sec. 3. The way in which these agents manage the dynamic information ﬂows is described in Sec. 4. The interaction of more than one of these agents engaging in competitive negotiation is described in Sec. 5. Sec. 6 concludes. 1 2

http://e-markets.org.au http://www.iiia.csic.es/

M. Klusch, M. Rovatsos, and T. Payne (Eds.): CIA 2006, LNAI 4149, pp. 51–65, 2006. c Springer-Verlag Berlin Heidelberg 2006

52

2

J. Debenham and S. Simoﬀ

Data Mining

We have designed information discovery and delivery agents that utilise text and network data mining for supporting real-time negotiation. This work has addressed the central issues of extracting relevant information from diﬀerent on-line repositories with diﬀerent Fig. 1. Information impacts trading formats, with possible duplicative and erroneous data. That is, we have addressed the central issues in extracting information from the World Wide Web. Our mining agents understand the inﬂuence that extracted information has on the subject of negotiation and takes that in account. Real-time embedded data mining is an essential component of the proposed framework. In this framework the trading agents make their informed decisions, based on utilising two types of information (as illustrated in Figure 1): – information extracted from the negotiation process (i.e. from the exchange of oﬀers), and; – information from external sources, extracted and provided in condensed form. The embedded data mining system provides the information extracted from the external sources. The system complements and services the information-based architecture developed in [4] and [5]. The information request and the information delivery format is deFig. 2. Pipeline for “focused” data sets ﬁned by the interaction ontology. As these agents operate with negotiation parameters with a discrete set of feasible values, the information request is formulated in terms of these values. As agents proceed with negotiation they have a topic of negotiation and a shared ontology that describes that topic. For example, if the topic of negotiation is buying a number of digital cameras for a University, the shared ontology will include the product model of the camera, and some characteristics, like “product reputation” (which on their own can be a list of parameters), that are usually derived from additional sources (for example, from diﬀerent opinions in a professional community of photographers or digital artists). As the information-based architecture assumes that negotiation parameters are discrete, the information request can be formulated as a subset of the range of values for a negotiation parameter. For example, if the negotiator is interested in cameras with 8 megapixel resolution, and the brand is a negotiation parameter, the information request can be formulated as a set of camera models, e.g. {“Canon Power Shot

Agents for Information-Rich Environments

53

Pro 1”, “Sony f828”, “Nikon Coolpix 8400”, “Olympus C-8080”} and a preference estimate based on the information in the diﬀerent articles available. The collection of parameter sets of the negotiation topic constitutes the input to the data mining system. Continuous numerical values are replaced by ﬁnite number of ranges of interest. The data mining system initially constructs data sets that are “focused” on requested information, as illustrated in Figure 2. From the vast amount of information available in electronic form, we need to ﬁlter the information that is relevant to the information request. In our example, this will be the news, opinions, comments, white papers related to the ﬁve models of digital cameras. Technically, the automatic retrieval of the information pieces utilises the universal news bot architecture presented in [1]. Developed originally for news sites only, the approach is currently being extended to discussion boards and company white papers. The “focused” data set is dynamically constructed in an iterative process. The data mining agent constructs the news data set according to the concepts in the query. Each concept is represented as a cluster of key terms (a term can include one or more words), deﬁned by the proximity position of the frequent key terms. On each iteration the most frequent (terms) from the Fig. 3. Architecture of data mining system retrieved data set are extracted and considered to be related to the same concept. The extracted keywords are resubmitted to the search engine. The process of query submission, data retrieval and keyword extraction is repeated until the search results start to derail from the given topic. The set of topics in the original request is used as a set of class labels. In our example we are interested in the evidence in support of each particular model camera model. A simple solution is for each model to introduce two labels — positive opinion and negative opinion, ending with ten labels. In the constructed “focused” data set, each news article is labelled with one of the values from this set of labels. An automated approach reported in [1] extends the tree-based approach proposed in [6]. The data sets required further automatic pre-processing, related to possible redundancies in the information encoded in the set that can bias the analysis algorithms. For example, identifying a set of opinions about the camera that most likely comes from the same author, though it has been retrieved from diﬀerent “opinion boards” on the Internet.

54

J. Debenham and S. Simoﬀ

Once the set is constructed, building the “advising model” is reduced to a classiﬁcation data mining problem. As the model is communicated back to the information-based agent architecture, the classiﬁer output should include all the possible class labels with an attached probability estimates for each class. Hence, we use probabilistic classiﬁers (e.g. Na¨ıve Bayes, Bayesian Network classiﬁers) [7] without the min-max selection of the class output [e.g., in a classiﬁer based on Na¨ıve Bayes algorithm], we calculate the posterior probability Pp (i) of each class c(i) with respect to combinations of key terms and then return the tuples < c(i), Pp (i) > for all classes, not just the one with maximum Pp (i). In the case when we deal with range variables the data mining system returns the range within which is the estimated value. For example, the response to a request for an estimate of the rate of change between two currencies over speciﬁed period of time will be done in three steps: (i) the relative focused news data set will be updated for the speciﬁed period; (ii) the model that takes these news in account is updated, and; (iii) the output of the model is compared with requested ranges and the matching one is returned. The details of this part of the data mining system are presented in [8]. The currently used model is a modiﬁed linear model with an additional term that incorporates a news index Inews, which reﬂects the news eﬀect on exchange rate. The current architecture of the data mining system in the e-market environment is shown in Figure 3. The {θ1 , . . . , θt } denote the output of the system to the information-based agent architecture. In addition, the data mining system provides parameters that deﬁne the “quality of the information”, including: – the time span of the “focused” data set, (deﬁned by the eldest and the latest information unit); – estimates of the characteristics of the information sources, including reliability, trust and cost, that then are used by the information-based agent architecture. Overall the parameters that will be estimated by the mining algorithms and provided to the negotiating agents are expected to allow information-based agents to devise more eﬀective and better informed situated strategies. In addition to the data coming from external sources, the data mining component of the project will develop techniques for analysing agent behaviourist data with respect to the electronic institution set-up.

3

Information-Based Agents

We have designed a new agent architecture founded on information theory. These “information-based” agents operate in real-time in response to market information ﬂows. We have addressed the central issues of trust in the execution of contracts, and the reliability of information [5]. Our agents understand the value of building business relationships as a foundation for reliable trade. An inherent diﬃculty in automated trading — including e-procurement — is that it is generally multi-issue. Even a simple trade, such as a quantity of steel, may involve:

Agents for Information-Rich Environments

55

delivery date, settlement terms, as well as price and the quality of the steel. The “information-based” agent’s reasoning is based on a ﬁrst-order logic world model that manages multi-issue negotiation as easily as single-issue. Most of the work on multi-issue negotiation has focussed on one-to-one bargaining — for example [9]. There has been rather less interest in one-to-many, multi-issue auctions — despite the size of the e-procurement market which typically attempts to extend single-issue, reverse auctions to the multi-issue case by post-auction haggling. There has been even less interest in many-to-many, multi-issue exchanges. The generic architecture of our “information-based” agents is presented in Sec. 3.1. The agent’s reasoning employs entropy-based inference and is described in Sec. 3.2. The integrity of the agent’s information is in a permanent state of decay, Sec. 4 describes the agent’s machinery for managing this decay leading to a characterisation of the “value” of information. 3.1

Agent Architecture

This section describes the essence of “information-based agency”. An agent observes events in its environment including what other agents actually do. It chooses to represent some of those observations in its world model as beliefs. As time passes, an agent may not be prepared to accept such beliefs as being “true”, and qualiﬁes those representations with epistemic probabilities. Those qualiﬁed representations of prior observations are the agent’s information. This information is primitive — it is the agent’s representation of its beliefs about prior events in the environment and about the other agents prior actions. It is independent of what the agent is trying to achieve, or what the agent believes the other agents are trying to achieve. Given this information, an agent may then choose to adopt goals and strategies. Those strategies may be based on game theory, for example. To enable the agent’s strategies to make good use of its information, tools from information theory are applied to summarise and process that information. Such an agent is called information-based. An agent called Π is the subject of this discussion. Π engages in multi-issue negotiation with a set of other agents: {Ω1 , · · · , Ωo }. The foundation for Π’s operation is the information that is generated both by and because of its negotiation exchanges. Any message from one agent to another reveals information about the sender. Π also acquires information from the environment — including general information sources — to support its actions. Π uses ideas from information theory to process and summarise its information. Π’s aim may not be “utility optimisation” — it may not be aware of a utility function. If Π does know its utility function and if it aims to optimise its utility then Π may apply the principles of game theory to achieve its aim. The information-based approach does not reject utility optimisation — in general, the selection of a goal and strategy is secondary to the processing and summarising of the information. In addition to the information derived from its opponents, Π has access to a set of information sources {Θ1 , · · · , Θt } that may include the marketplace in which trading takes place, and general information sources such as news-feeds

56

J. Debenham and S. Simoﬀ

accessed via the Internet. Together, Π, {Ω1 , · · · , Ωo } and {Θ1 , · · · , Θt } make up a multiagent system. The integrity of Π’s information, including information extracted from the Internet, will decay in time. The way in which this decay occurs will depend on the type of information, and on the source from which it was drawn. Little appears to be known about how the integrity of real information, such as news-feeds, decays, although its validity can often be checked — “Is company X taking over company Y?” — by proactive action given a cooperative information source Θj . So Π has to consider how and when to refresh its decaying information. Π has two languages: C and L. C is an illocutionary-based language for communication. L is a ﬁrst-order language for internal representation — precisely it is a ﬁrst-order language with sentence probabilities optionally attached to each sentence representing Π’s epistemic belief in the truth of that sentence. Fig. 4 shows a high-level view of how Π operates. Messages expressed in C from {Θi } and {Ωi } are received, time-stamped, source-stamped and placed in an in-box X . The messages in X are then translated using an import function I into sentences expressed in L that have integrity decay functions (usually of time) attached to each sentence, they are stored in a repository Y t . And that is all that happens until Π triggers a goal. Π triggers a goal, g ∈ G, in Information Institution Other Principal Sources agent agents two ways: ﬁrst in response to a ξ message received from an oppoθ1 , . . , θt Ω1 , . . , Ωo nent {Ωi } “I oﬀer you e1 in exchange for an apple”, and second I Y t X N in response to some need, ν ∈ N , “goodness, we’ve run out of cofG T M fee”. In either case, Π is motivated G by a need — either a need to strike s ∈ S a deal with a particular feature Js (such as acquiring coﬀee) or a genbs W eral need to trade. Π’s goals could Z be short-term such as obtaining information “what is the time?”, Agent Π z medium-term such as striking a deal with one of its opponents, or, Fig. 4. Architecture of agent Π rather longer-term such as building a (business) relationship with one of its opponents. So Π has a trigger mechanism T where: T : {X ∪ N } → G. For each goal that Π commits to, it has a mechanism, G, for selecting a strategy to achieve it where G : G × M → S where S is the strategy library. A strategy s maps an information base into an action, s(Y t ) = z ∈ Z. Given a goal, g, and the current state of the social model mt , a strategy: s = G(g, mt ). Each strategy, s, consists of a plan, bs and a world model (construction and revision) function, Js , that constructs, and maintains the currency of, the strategy’s world model Wst that consists of a set of probability distributions. A plan derives the

Agents for Information-Rich Environments

57

agent’s next action, z, on the basis of the agent’s world model for that strategy and the current state of the social model: z = bs (Wst , mt ), and z = s(Y t ). Js employs two forms of entropy-based inference: – Maximum entropy inference, Js+ , ﬁrst constructs an information base Ist as a set of sentences expressed in L derived from Y t , and then from Ist constructs the world model, Wst , as a set of complete probability distributions. – Given a prior world model, Wsu , where u < t, minimum relative entropy (u,t) inference, Js− , ﬁrst constructs the incremental information base Is of sent tences derived from those in Y that were received between time u and time (u,t) t, and then from Wsu and Is constructs a new world model, Wst . 3.2

Π’s Reasoning

Once Π has selected a plan a ∈ A it uses maximum entropy inference to derive the {Dis }ni=1 [see Fig. 4] and minimum relative entropy inference to update those distributions as new data becomes available. Entropy, H, is a measure of uncertainty [10] in a probability distribution for a discrete random variable X: H(X) − i p(xi ) log p(xi ) where p(xi ) = P(X = xi ). Maximum entropy inference is used to derive sentence probabilities for that which is not known by constructing the “maximally noncommittal” probability distribution, and is chosen for its ability to generate complete distributions from sparse data. Let G be the set of all positive ground literals that can be constructed using Π’s language L. A possible world, v, is a valuation function: G → {, ⊥}. V|Ks = {vi } is the set of all possible worlds that are consistent with Π’s knowledge base Ks that contains statements which Π believes are true. A random world for Ks , W |Ks = {pi } is a probability distribution over V|Ks = {vi }, where pi expresses Π’s degree of belief that each of the possible worlds, vi , is the actual world. The derived sentence probability of any σ ∈ L, with respect to a random world W |Ks is: (∀σ ∈ L)P{W |Ks } (σ) { pn : σ is in vn } (1) n

Bts

{Ωj }M j=1

The agent’s belief set = contains statements to which Π attaches a given sentence probability B(.). A random world W |Ks is consistent with Bts if: (∀Ω ∈ Bts )(B(Ω) = P{W |Ks } (Ω)). Let {pi } = {W |Ks , Bts } be the “maximum entropy probability distribution over V|Ks that is consistent with Bts ”. Given an agent with Ks and Bts , maximum entropy inference states that the derived sentence probability for any sentence, σ ∈ L, is: (∀σ ∈ L)P{W |Ks ,Bs } (σ) { pn : σ is in vn } (2) t

n

From Eqn. 2, each belief imposes a linear constraint on the {pi }. The maximum entropy distribution: arg maxp H(p), p = (p1 , . . . , pN ), subject to M + 1 linear constraints: gj (p) =

N i=1

cji pi − B(Ωj ) = 0,

j = 1, . . . , M.

g0 (p) =

N i=1

pi − 1 = 0

58

J. Debenham and S. Simoﬀ

where cji = 1 if Ωj is in vi and 0 otherwise, and pi ≥ 0, i = 1, . . . , N , is found by introducing Lagrange multipliers, and then obtaining a numerical solution using the multivariate Newton-Raphson method. In the subsequent subsections we’ll see how an agent updates the sentence probabilities depending on the type of information used in the update. Given a prior probability distribution q = (qi )ni=1 and a set of constraints C, the principle of minimum relative entropy chooses the posterior probability distribution p = (pi )ni=1 that has the least relative entropy 3 with respect to q: {W |q, C} arg min p

n i=1

pi log

pi qi

and that satisﬁes the constraints. This may be found by introducing Lagrange multipliers as above. Given a prior distribution q over {vi } — the set of all possible worlds, and a set of constraints C (that could have been derived as above from a set of new beliefs) minimum relative entropy inference states that the derived sentence probability for any sentence, σ ∈ L, is: (∀σ ∈ L)P{W |q,C} (σ) { pn : σ is in vn } (3) n

where {pi } = {W |q, C}. The principle of minimum relative entropy is a generalisation of the principle of maximum entropy. If the prior distribution q is uniform, then the relative entropy of p with respect to q, p q, diﬀers from −H(p) only by a constant. So the principle of maximum entropy is equivalent to the principle of minimum relative entropy with a uniform prior distribution.

4

Managing Dynamic Information Flows

The illocutions in the communication language C include information, [info]. The information received from general information sources will be expressed in terms deﬁned by Π’s ontology. We assume that Π makes at least part of that ontology public so that the other agents {Ω1 , . . . , Ωo } may communicate [info] that Π can understand. Ω’s reliability is an estimate of the extent to which this [info] is correct. For example, Ω may send Π the [info] that “the price of ﬁsh will go up by 10% next week”, and it may actually go up by 9%. The only restriction on incoming [info] is that it is expressed in terms of the ontology — this is very general. However, the way in which [info] is used is completely speciﬁc — it will be represented as a set of linear constraints on one or more probability distributions. A chunk of [info] may not be directly related to one of Π’s chosen distributions or may not be expressed naturally as constraints, and so some inference machinery is required to derive these constraints — this inference is performed by model building functions, Js , that have been activated by a plan s chosen by Π. JsD ([info]) denotes the set of constraints on distribution D derived by Js from [info]. 3

Otherwise called cross entropy or the Kullback-Leibler distance between the two probability distributions.

Agents for Information-Rich Environments

4.1

59

Updating the World Model with [info]

The procedure for updating the world model as [info] is received follows. If at time u, Π receives a message containing [info] it is time-stamped and sourcestamped [info](Ω,Π,u) , and placed in a repository Y t . If Π has an active plan, s, with model building function, Js , then Js is applied to [info](Ω,Π,u) to derive constraints on some, or none, of Π’s distributions. The extent to which those constraints are permitted to eﬀect the distributions is determined by a value for the reliability of Ω, Rt (Π, Ω, O([info])), where O([info]) is the ontological context of [info]. An agent may have models of integrity decay for some particular distributions, but general models of integrity decay for, say, a chunk of information taken at random from the World Wide Web are generally unknown. However the values to which decaying integrity should tend in time are often known. For example, a prior value for the truth of the proposition that a “22 year-old male will default on credit card repayment” is well known to banks. If Π attaches such prior values n to a distribution D they are called the decay limit distribution for D, (dD i )i=1 . No matter how integrity of [info] decays, in the absence of any other relevant information it should decay to the decay limit distribution. If a distribution with n values has no decay limit distribution then integrity decays to the maximum entropy value n1 . In other words, the maximum entropy distribution is the default decay limit distribution. In the absence of new [info] the integrity of distributions decays. If D = (qi )ni=1 then we use a geometric model of decay: D t qit+1 = (1 − ρD ) × dD i + ρ × qi , for i = 1, . . . , n

(4)

where ρ ∈ (0, 1) is the decay rate. This raises the question of how to determine ρD . Just as an agent may know the decay limit distribution it may also know something about ρD . In the case of an information-overfed agent there is no harm in conservatively setting ρD “a bit on the low side” as the continually arriving [info] will sustain the estimate for D. We now describe how new [info] is imported to the distributions. A single chunk of [info] may eﬀect a number of distributions. Suppose that a chunk of [info] is received from Ω and that Π attaches the epistemic belief probability Rt (Π, Ω, O([info])) to it. Each distribution models a facet of the world. Given a distribution Dt = (qit )ni=1 , qit is the probability that the possible world ωi for D is the true world for D. The eﬀect that a chunk [info] has on distribution D is to enforce the set of linear constraints on D, JsD ([info]). If the constraints JsD ([info]) are taken by Π as valid then Π could update D to the posterior [info] n distribution (pi )i=1 that is the distribution with least relative entropy with respect to (qit )ni=1 satisfying the constraint: [info] {pi : JsD ([info]) are all in ωi } = 1. (5) D

i

But R (Π, Ω, O([info])) = r ∈ [0, 1] and Π should only treat the JsD ([info]) as valid if r = 1. In general r determines the extent to which the eﬀect of [info] on t

60

J. Debenham and S. Simoﬀ [info] n )i=1

D is closer to (pi

or to the prior (qit )ni=1 distribution by: [info]

pti = r × pi

+ (1 − r) × qit

(6)

But, we should only permit a new chunk of [info] to inﬂuence D if doing so gives us new information. For example, if 5 minutes ago a trusted agent advises Π that the interest rate will go up by 1%, and 1 minute ago a very unreliable agent advises Π that the interest rate may go up by 0.5%, then the second unreliable chunk should not be permitted to ‘overwrite’ the ﬁrst. We capture this by only permitting a new chunk of [info] to be imported if the resulting distribution has more information relative to the decay limit distribution than the existing distribution has. Precisely, this is measured using the Kullback-Leibler distance measure — this is just one criterion for determining whether the [info] should be used — and [info] is only used if: n

pti qit > qit log D D di di i=1 n

pti log

i=1

(7)

In addition, we have described in Eqn. 4 how the integrity of each distribution D will decay in time. Combining these two into one result, distribution D is revised to: D t (1 − ρD ) × dD if usable [info] is received at time t i + ρ × pi qit+1 = D D D t (1 − ρ ) × di + ρ × qi otherwise for i = 1, · · · , n, and decay rate ρD as before. 4.2

Information Reliability

We estimate Rt (Π, Ω, O([info])) by measuring the error in information. Π’s plans will have constructed a set of distributions. We measure the ‘error’ in information as the error in the eﬀect that information has on each of Π’s distributions. Suppose that a chunk of [info] is received from agent Ω at time s and is veriﬁed at some later time t. For example, a chunk of information could be “the interest rate will rise by 0.5% next week”, and suppose that the interest rate actually rises by 0.25% — call that correct information [fact]. What does all this tell agent Π about agent Ω’s reliability? Consider one of Π’s distributions D that [info] n is {qis } at time s. Let (pi )i=1 be the minimum relative entropy distribution [fact] n given that [info] has been received as calculated in Eqn. 5, and let (pi )i=1 be that distribution if [fact] had been received instead. Suppose that the reliability s s estimate for distribution D was RD . This section is concerned with what RD should have been in the light of knowing now, at time t, that [info] should have been [fact], and how that knowledge eﬀects our current reliability estimate for D, Rt (Π, Ω, O([info])). The idea of Eqn. 6, is that the current value of r should be such that, on [fact] n average, (psi )ni=1 will be seen to be “close to” (pi )i=1 when we eventually

Agents for Information-Rich Environments

61

discover [fact] — no matter whether or not [info] was used to update D, as determined by the acceptability test in Eqn. 7 at time s. That is, given [info], [info] n [fact] n [fact] and the prior (qis )ni=1 , calculate (pi )i=1 and (pi )i=1 using Eqn. 5. ([info]|[fact]) Then the observed reliability for distribution D, RD , on the basis of the veriﬁcation of [info] with [fact] is the value of r that minimises the Kullback[fact] n Leibler distance between (psi )ni=1 and (pi )i=1 : arg min r

n i=1

[info]

[info]

(r · pi

+ (1 − r) · qis ) log

r · pi

+ (1 − r) · qis [fact]

pi

If E [info] is the set of distributions that [info] eﬀects, then the overall observed reliability on the basis of the veriﬁcation of [info] with [fact] is: R([info]|[fact]) = ([info]|[fact]) 1 − (maxD∈E [info] |1 − RD |). Then for each ontological context oj , at time t when, perhaps, a chunk of [info], with O([info]) = ok , may have been veriﬁed with [fact]: Rt+1 (Π, Ω, oj ) = (1 − ρ) × Rt (Π, Ω, oj ) + ρ × R([info]|[fact]) × Sem(oj , ok ) where Sem(·, ·) : O × O → [0, 1] measures the semantic distance between two sections of the ontology, and ρ is the learning rate. Over time, Π notes the ontological context of the various chunks of [info] received from Ω and over the various ontological contexts calculates the relative frequency, P t (oj ), of these contexts, oj = O([info]). This leads to a overall expectation of the reliability that agent Π has for agent Ω: Rt (Π, Ω) = j P t (oj ) × Rt (Π, Ω, oj ).

5

Negotiation

For illustration Π’s communication language [11] is restricted to the illocutions: Oﬀer(·), Accept(·), Reject(·) and Withdraw(·). The simple strategies that we will describe all use the same world model function, Js , that maintains the following two probability distributions as their world model: – Pt (ΠAcc(Π, Ω, ν, δ)) — the strength of belief that Π has in the proposition that she should accept the proposal δ = (a, b) from agent Ω in satisfaction of need ν at time t, where a is Π’s commitment and b is Ω’s commitment. Pt (ΠAcc(Π, Ω, ν, δ)) is estimated from: 1. Pt (Satisfy(Π, Ω, ν, δ)) a subjective evaluation (the strength of belief that Π has in the proposition that the expected outcome of accepting the proposal will satisfy some of her needs). 2. Pt (Fair(δ)) an objective evaluation (the strength of belief that Π has in the proposition that the proposal is a “fair deal” in the open market. 3. Pt (ΠCanDo(a) an estimate of whether Π will be able to meet her commitment a at contract execution time. These three arrays of probabilities are estimated by importing relevant information, [info], as described in Sec. 4.

62

J. Debenham and S. Simoﬀ

– Pt (ΩAcc(β, α, δ)) — the strength of belief that Π has in the proposition that Ω would accept the proposal δ from agent Π at time t. Every time that Ω submits a proposal she is revealing information about what she is prepared to accept, and every time she rejects a proposal she is revealing information about what she is not prepared to accept. Eg: having received the stamped illocution Oﬀer(Ω, Π, δ)(Ω,Π,u) , at time t > u, Π may believe that Pt (ΩAcc(Ω, Π, δ)) = κ this is used as a constraint on Pt+1 (ΩAcc(·)) which is calculated using Eqn. 3. Negotiation is an information revelation process. The agents described in Sec. 3 are primarily concerned with the integrity of their information, and then when they have acquired suﬃcient information to reduce their uncertainty to an acceptable level they are concerned with acting strategically within the surrounding uncertainty. A basic conundrum in any oﬀer-exchange bargaining is: it is impossible to force your opponent to reveal information about their position without revealing information about your own position. Further, by revealing information about your own position you may change your opponents position — and so on.4 This inﬁnite regress, of speculation and counter-speculation, is avoided here by ignoring the internals of the opponent and by focussing on what is known for certain — that is: what information is contained in the signals received and when did those signals arrive. 5.1

Utility-Based Strategies

An agent’s strategy s is a function of the information Y t that it has at time t. Four simple strategies make oﬀers only on the basis of Pt (ΠAcc(Π, Ω, ν, δ)), Π’s acceptability threshold γ, and Pt (ΩAcc(Ω, Π, δ)). The greedy strategy s+ chooses: arg max{Pt (ΠAcc(Π, Ω, ν, δ)) | Pt (ΩAcc(Ω, Π, δ)) 0}, δ

it is appropriate when Π believes Ω is desperate to trade. The expected-acceptability-to-Π-optimising strategy s∗ chooses: arg max{Pt (ΩAcc(Ω, Π, δ)) × Pt (ΠAcc(Π, Ω, ν, δ)) | Pt (ΠAcc(Π, Ω, ν, δ)) ≥ γ} δ

when Π is conﬁdent and not desperate to trade. The strategy s− chooses: arg max{Pt (ΩAcc(Ω, Π, δ)) | Pt (ΠAcc(Π, Ω, ν, δ)) ≥ γ} δ

it optimises the likelihood of trade — when Π is keen to trade without compromising its own standards of acceptability. An approach to issue-tradeoﬀs is described in [9]. The bargaining strategy described there attempts to make an acceptable oﬀer by “walking round” the 4

This a reminiscent of Werner Heisenberg’s indeterminacy relation, or unbestimmtheitsrelationen: “you can’t measure one feature of an object without changing another” — with apologies.

Agents for Information-Rich Environments

63

iso-curve of Π’s previous oﬀer δ (that has, say, an acceptability of γδ ≥ γ) towards Ω’s subsequent counter oﬀer. In terms of the machinery described here, an analogue is to use the strategy s− : arg max{Pt (ΩAcc(Ω, Π, δ)) | Pt (ΠAcc(Π, Ω, ν, δ)) ≥ γδ } δ

with γ = γδ . This is reasonable for an agent that is attempting to be accommodating without compromising its own interests. The complexity of the strategy in [9] is linear with the number of issues. The strategy described here does not have that property, but it beneﬁts from using Pt (ΩAcc(Ω, Π, δ)) that contains foot prints of the prior oﬀer sequence — estimated by repeated use of Eqn. 3 — in that distribution more recent data gives estimates with greater certainty. 5.2

Information-Based Strategies

Π’s negotiation strategy is a function s : Y t → Z where Z is the set of actions [see Fig. 4] that send Oﬀer(.), Accept(.), Reject(.) and Quit(.) messages to Ω. If Π sends Oﬀer(.), Accept(.) or Reject(.) messages to Ω then she is giving Ω information about herself. In an inﬁnite-horizon bargaining game where there is no incentive to trade now rather than later, a self-interested agent will “sit and wait”, and do nothing except, perhaps, to ask for information. The well known bargaining response to an approach by an interested party “Well make me an oﬀer” illustrates how a shrewd bargainer may behave in this situation. An agent may be motivated to act for various reasons — three are mentioned. First, if there are costs involved in the bargaining process due either to changes in the value of the negotiation object with time or to the intrinsic cost of conducting the negotiation itself. Second, if there is a risk of breakdown caused by the opponent walking away from the bargaining table. Third, if the agent is concerned with establishing a sense of trust [12] with the opponent —this could be the case in the establishment of a business relationship. Of these three reasons the last two are addressed here. The risk of breakdown may be reduced, and a sense of trust may be established, if the agent appears to its opponent to be “approaching the negotiation in an even-handed manner”. One dimension of “appearing to be even-handed” is to be equitable with the value of information given to the opponent. Various bargaining strategies, both with and without breakdown, are described in [4], but they do not address this issue. A bargaining strategy is described here that is founded on a principle of “equitable information gain”. That is, Π attempts to respond to Ω’s messages so that Ω’s expected information gain similar to that which Π has received. Π models Ω by observing her actions, and by representing beliefs about her future actions in the probability distribution P(ΩAcc(·)). Π measures the value of information that it receives from Ω by the change in the entropy of this distribution as a result of representing that information in P(ΩAcc(·)). More generally, Π measures the value of information received in a message, μ, by the change in the entropy in its entire representation, s Wst , as a result of the receipt

64

J. Debenham and S. Simoﬀ

of that message; this is denoted by: Δμ |W t |, where |W t | denotes the value of the uncertainty (as negative entropy) in Π’s information in W t . Although both Π and Ω will build their models of each other using the same data — the messages exchanged — the observed information gain will depend on the way in which each agent has represented this information as discussed in [4]. It is “not unreasonable to suggest” that these two representations should be similar. To support Π’s attempts to achieve “equitable information gain” she assumes that Ω’s reasoning apparatus mirrors her own, and so is able to estimate the change in Ω’s entropy as a result of sending a message μ to Ω: Δμ |WΩt |. Suppose that Π receives a message μ = Oﬀer(.) from Ω and observes an information gain of Δμ |W t |. Suppose that Π wishes to reject this oﬀer by sending a counter-oﬀer, Oﬀer(δ), that will give Ω expected “equitable information gain”. δ = {arg max P(ΠAcc(δ) | Y t ) ≥ α | (ΔOﬀer(δ) |WΩt | ≈ Δμ |W t |)}. δ

That is Π chooses the most acceptable deal to herself that gives her opponent expected “equitable information gain” provided that there is such a deal. If there is not then Π chooses the best available compromise δ = {arg max(ΔOﬀer(δ) |WΩt |) | P(ΠAcc(δ) | Y t ) ≥ α} δ

provided there is such a deal — this strategy is rather generous, it rates information gain ahead of personal acceptability. If there is not then Π does nothing. The “equitable information gain” strategy generalises the simple-minded alternating oﬀers strategy. Suppose that Π is trying to buy something from Ω with bilateral bargaining in which all oﬀers and responses stand — ie: there is no decay of oﬀer integrity. Suppose that Π has oﬀered $1 and Ω has refused, and Ω has asked $10 and Π has refused. If amounts are limited to whole dollars only then the deal set D = {1, · · · , 10}. Π models Ω with the distribution P(ΩAcc(.)), and knows that P(ΩAcc(1)) = 0 and P(ΩAcc(10)) = 1. The remaining eight values in this distribution are provided by the inference procedure — see Sec. 3.2 — and the entropy of the resulting distribution is 2.2020. To apply the “equitable information gain” strategy Π assumes that Ω’s decision-making machinery mirrors its own. In which case Ω is assumed to have constructed a mirror-image distribution to model Π that will have the same entropy. At this stage, time t = 0, calibrate the amount of information held by each agent at zero — ie: |W 0 | = |WΩ0 | = 0. Now if, at time t = 1, Ω asks Π for $9 then Ω gives information to Π and |W 1 | = 0.2548. If Π rejects this oﬀer then she gives information to Ω and |WΩ1 | = 0.2548. Suppose that Π wishes to counter with an “equitable information gain” oﬀer. If, at time t = 2, Π oﬀers Ω $2 then |WΩ2 | = 0.2548 + 0.2559. Alternatively, if Π oﬀers Ω $3 then |WΩ2 | = 0.2548 + 0.5136. And so $2 is a near “equitable information gain” response by Π at time t = 2. Entropy-based inference operates naturally with multi-issue oﬀers where this calculation becomes considerably more interesting.

Agents for Information-Rich Environments

6

65

Conclusions

Electronic marketplaces are awash with dynamic information ﬂows including hard market data and soft textual data such as news feeds. Structured and unstructured data mining methods identify, analyse, condense and deliver signals from this data whose integrity may be questionable. An ‘information-based’ agent architecture has been described, that is founded on ideas from information theory, and has been developed speciﬁcally to operate with the data mining system. It is part of our eMarket platform1 that also includes a “virtual institution” system in a collaborative research project with “Institut d’Investigacio en Intel.ligencia Artiﬁcial”, Spanish Scientiﬁc Research Council, UAB, Barcelona.

References 1. Zhang, D., Simoﬀ, S.: Informing the Curious Negotiator: Automatic news extraction from the Internet. In Williams, G., Simoﬀ, S., eds.: Data Mining: Theory, Methodology, Techniques, and Applications. Springer-Verlag: Heidelberg, Germany (2006) 176 – 191 2. Wooldridge, M.: Multiagent Systems. Wiley (2002) 3. Simoﬀ, S., Debenham, J.: Curious negotiator. In M. Klusch, S.O., Shehory, O., eds.: Proceedings 6th International Workshop Cooperative Information Agents VI CIA2002, Madrid, Spain, Springer-Verlag: Heidelberg, Germany (2002) 104–111 4. Debenham, J.: Bargaining with information. In Jennings, N., Sierra, C., Sonenberg, L., Tambe, M., eds.: Proceedings Third International Conference on Autonomous Agents and Multi Agent Systems AAMAS-2004, ACM Press, New York (2004) 664 – 671 5. Sierra, C., Debenham, J.: An information-based model for trust. In Dignum, F., Dignum, V., Koenig, S., Kraus, S., Singh, M., Wooldridge, M., eds.: Proceedings Fourth International Conference on Autonomous Agents and Multi Agent Systems AAMAS-2005, Utrecht, The Netherlands, ACM Press, New York (2005) 497 – 504 6. Reis, D., Golgher, P.B., Silva, A., Laender, A.: Automatic web news extraction using tree edit distance. In: Proceedings of the 13th International Conference on the World Wide Web, New York (2004) 502–511 7. Ramoni, M., Sebastiani, P.: Bayesian methods. In: Intelligent Data Analysis. Springer-Verlag: Heidelberg, Germany (2003) 132–168 8. Zhang, D., Simoﬀ, S., Debenham, J.: Exchange rate modelling using news articles and economic data. In: Proceedings of The 18th Australian Joint Conference on Artiﬁcial Intelligence, Sydney, Australia, Springer-Verlag: Heidelberg, Germany (2005) 9. Faratin, P., Sierra, C., Jennings, N.: Using similarity criteria to make issue tradeoﬀs in automated negotiation. Journal of Artiﬁcial Intelligence 142 (2003) 205–237 10. MacKay, D.: Information Theory, Inference and Learning Algorithms. Cambridge University Press (2003) 11. Jennings, N., Faratin, P., Lomuscio, A., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: Prospects, methods and challenges. International Journal of Group Decision and Negotiation 10 (2001) 199–215 12. Ramchurn, S., Jennings, N., Sierra, C., Godo, L.: A computational trust model for multi-agent interactions based on conﬁdence and reputation. In: Proceedings 5th Int. Workshop on Deception, Fraud and Trust in Agent Societies. (2003)

Information Agents for Optimal Repurposing and Personalization of Web Contents in Semantics-Aware Ubiquitous and Mobile Computing Environments Fernando Alonso, Sonia Frutos, Miguel Jim´enez, and Javier Soriano School of Computer Science, Universidad Polit´ecnica de Madrid 28660 - Boadilla del Monte, Madrid, Spain {falonso, sfrutos, jsoriano}@fi.upm.es, [email protected]

Abstract. Web contents repurposing and personalization is becoming crucial for enabling ubiquitous Web access from a wide range of mobile devices under varying conditions that may depend on device capabilities, network connectivity, navigation context, user preferences, user disabilities and existing social conventions. Semantic annotations can provide additional information so that a content adaptation engine, based on the holistic integration of both Information Agents and Semantic Web technologies, can make better decisions, leading to optimal results in terms of legibility and usability. Bearing this in mind, this paper presents the rationale behind MorfeoSMC: an open source mobility platform that enables the development of semantics-aware mobile applications and services in order to provide improved Web accessibility and increase social inclusion. In particular, the paper focuses on how MorfeoSMC information agents tackle the use of semantic markup in the information rendered for users through the mobility platform and as part of a user-interest proﬁleaware and navigation context-aware Web content adaptation process. It also presents an innovative semantic matching framework that is at the core of this semantics-aware Web content adaptation process.

1

Introduction

With the advent of emerging personal computing paradigms, such as ubiquitous and mobile computing, Web contents are becoming accessible from a wide and evolving range of Web-enabled mobile devices, ranging from WAP-enabled phones, through PDAs, to in-car navigation devices. All of these devices have diﬀerent rendering capabilities than traditional desktop computers and a range of settings, functionality and rendering options that place heavy demands on both interoperability and accessibility. This means that Web contents need to be (a) repurposed for transparent access from a variety of client agents, and (b) personalized, enabling real-time context-aware delivery of content speciﬁc to and/or of interest to the individual or machine visiting a website. Furthermore, M. Klusch, M. Rovatsos, and T. Payne (Eds.): CIA 2006, LNAI 4149, pp. 66–80, 2006. c Springer-Verlag Berlin Heidelberg 2006

Information Agents for Optimal Repurposing and Personalization

67

Web content repurposing and personalization (e.g. ﬁltering, reformatting, priorizing, rearranging etc.) is useful not only in mobility environments, but also for improving information access for disabled people, especially if they are using mobile devices, which have severe accessibility constraints. This content adaptation is thus crucial for universal Web access under a variety of conditions that may depend on device capabilities, network connectivity, navigation context, user preferences, user disabilities and even existing social conventions. There are now mechanisms (e.g. [1]) and commercial solutions (e.g. [2]) for automatically adapting Web contents within mobility, commonly termed transcoding [3]. The problem with the existing approaches is that they are based exclusively on page syntax and not on the speciﬁc meaning of the concepts addressed on these pages or on the semantics associated with the user-interest proﬁle (including both preferences and disabilities) and with the navigation context (e.g. time, location, connectivity, device capabilities, etc.). Therefore, current page transcoders for mobile devices only select and transform the content to be displayed to the user on the basis of the tags that deﬁne the page rendering, leading to suboptimal results in terms of legibility and usability. Of the available technologies, those associated with the autonomous agentbased computation paradigm [4,5] are precisely the ones that are better accepted for dealing with issues associated with ubiquitous and mobile computing, helping to conceive new models, techniques and applications for developing solutions with a higher level of automation, greater potential for interoperability within open environments and better capabilities of cooperation and adaptation. In this respect, intelligent agent technology and, particularly, information agents provide a series of new and exciting possibilities in the ﬁeld of mobile and ubiquitous Web access [6]. These include Web content repurposing, and Web content personalization. For our purposes, an information agent (i.e. an intelligent agent for the Internet [7]) is supposed to deal with the diﬃculties associated with the information overload of the user it represents, and on behalf of whom it pro-actively acquires, mediates, and maintains relevant information. Their ability to semantically broker information by resolving the information impedance of both information consumers and providers, and oﬀering value-added information services and products to the user is therefore implicit. An approach to Web content repurposing and personalization that takes into account, among others, semantics-aware preferences in the user-interest proﬁle preferences and substantive aspects related to the navigation context for optimal mobile Web access is a prominent example of such ability in ubiquitous and mobile environments. Moreover, the success of the process of incorporating information agents, capable of repurposing and personalizing Web contents for optimal use across the evolving range of Web-enabled mobile devices in ubiquitous and mobile environments largely depends on the use of Semantic Web technology. Semantic annotations can provide additional information not only about Web contents, but also about user proﬁles [8], navigation contexts [9], and policies representing social conventions [10] so that a content adaptation engine, based on Semantic Web

68

F. Alonso et al.

technologies, can make better decisions on content repurposing and personalization. Nevertheless this sets new challenges for semantic markup and reasoning in such speciﬁc Semantic Web technologies application areas, all of which have direct ramiﬁcations on the emerging ubiquitous and mobile computing paradigms: user proﬁle-awareness, navigation context-awareness and policy modeling and enforcement [6]. In the light of the advances achieved by the international research community in both the intelligent agents and the Semantic Web ﬁelds, the strategy followed for building the existing Web content adaptation engines should be reconsidered, and the possibility of including a holistic integration of information agents and Semantic Web technologies in the Web content repurposing and personalization process should be examined, as should the beneﬁts of such a decision. With this in mind, this paper presents the rationale behind an advanced components platform, called Morfeo Semantic Mobility Channel (MorfeoSMC), that enables the development of mobility applications and services and takes into account the above-mentioned points. The remainder of the paper is organized as follows. Section 2 presents the rationale behind the proposed Agent-based architecture. Section 3 reviews the Morfeo open source mobility platform (MorfeoMC) on which the proposal is based. Then section 4 describes the MorfeoSMC approach to incorporating semantic annotations about the renderings delivered by mobile applications and services developed based on this platform. Section 5 introduces the MorfeoSMC approach to semantics-aware adaptation (i.e. repurposing and personalization) of such Web contents derived from the notion of user proﬁle-awareness. It then goes on to present an innovative semantic matching framework based on the proposed conceptualization of the user proﬁle and gives an example of how it is used to match user preferences against trade services descriptions delivered in MorfeoSMC. Section 6 goes a step further by introducing the MorfeoSMC approach to navigation context-awareness to improve the Web content adaptation process. To prove the value of the work, section 7 describes how MorfeoSMC is being used to give such a widespread application as TPIs Yellow Pages mobility, as well as aiming to oﬀer search results with semantic information. Finally, we conclude this paper in section 8.

2

Agent-Based Architecture Overview

Separating the structure from the rendering will assist the migration to the Semantic Web, where agents will not only be able to glean information, but will also be capable of ﬁltering and selecting relevant content for devices with lower bandwidth or requiring diﬀerent navigation approaches, taking into account user interests and the navigation context, thereby providing improved access and increasing social inclusion. Following ideas from [11], the semantic annotation of a Web application offered on MorfeoSMC is done as two separate processes, as shown in Fig. 1. Each process focuses on a separate semantic annotation process for the Web contents

Information Agents for Optimal Repurposing and Personalization

69

and for the view components in a device-independent rendering. The annotation process A (see Fig. 1) makes this point and oﬀers a supervised description of the view components making up the Web applications in MorfeoSMC. The annotation process B shows the process of semantically describing the contents that will feed the Web applications. Their annotation is also a supervised process performed by a knowledge engineer. These contents are annotated depending on their speciﬁc domain and a change of contents domain inﬂuences the ontologies used to annotate the view. In the example shown in Fig. 1 contents concerningTrade Services (section 5.1) are annotated. Therefore, the ontologies describing the trade services are also used to describe the view rendering these services.

Fig. 1. Semantic Annotation Process in MorfeoSMC

To cope with the huge amount of available information and the constrained capabilities of mobile devices, the MorfeoSMC architecture is populated with a number of semantics-aware information agents, each one acting on the behalf of a user for the purpose of ﬁltering and selecting relevant mobile Web contents oﬀered over the platform. These agents take the semantic description of the Web contents available in the MorfeoSMC contents managers to modify, ﬁlter or rearrange the contents depending on their knowledge of the actual user (user-interest proﬁle) or the context in which the data are requested (navigation context). The personalization of contents will be all the more eﬀective the more knowledge the agent has about the user. However, this server-side approach has a big disadvantage as regards user privacy and security, which calls for a trade-oﬀ between privacy and personalization. With the aim of optimizing this trade-oﬀ, this paper suggests separating the tasks that are performed by agents on the MorfeoSMC server from those that are implemented as client device plug-ins. Heavy-weight or data bandwidth-intensive tasks are carried out by server-side information agents (e.g. restaurant list prioritizing and page rearranging according to user preferences and geographic nearness), whereas light-weight tasks or

70

F. Alonso et al.

tasks requiring more precise knowledge about the user are performed by clientside information agents (e.g. personal information form ﬁlling), always keeping more sensitive personal information inside the client device.

Fig. 2. Information Agents on the Client Device

Two pairs of agents take part in any user-server interaction: (a) two userinterest proﬁle related agents (the client-side information agent handles the user-interest proﬁle and the information about it that it shares with its serverside peer), and (b) two navigation context-aware information agents perform the client- and server-side navigation context-based personalization tasks. The following sections describe in detail how these agents do their jobs and what knowledge they use to complete their tasks.

3

Foundations of the Morfeo Mobility Channel Platform

The MorfeoSMC platform is based on an earlier release of an open source mobility platform called Mobility Channel (MorfeoMC)1 [13], formerly TIDMobile, which has proven to be useful for rapidly developing applications and services that can be used to create comprehensive and integrated mobile solutions while concealing the complexity involved in managing multiple devices. MorfeoMC performs all multi-device programming tasks, such as identifying the device, adapting the page and contents to ﬁt device technology, markup language and capabilities, and managing sessions using the technology available on each device. This platform is based on a channel model supported by the principles of Service Oriented Architecture (SOA). This means that the channel adapter, which has been developed to give access to mobile devices, is run on the front-end, 1

Both MorfeoSMC and MorfeoMC are being developed in the context of the Morfeo open source development community [12], led by Telef´ onica R & D and undertaken in collaboration with a number of universities.

Information Agents for Optimal Repurposing and Personalization

71

and does not aﬀect the back-end business logic that it is based on. The contact points between the mobile applications and the business logic are called Application Operatons (AOs). These AOs are invoked from the mobile application to access the back-end services and are, thus, the nexus points between the front-end channel adapter and the back-end business logic core. The mobile applications and services are composed of Rendering Operations (ROs). An RO is a set of renderings or screens that are designed to perform a use case. The renderings are deﬁned in an XML-based grammar called Rendering Deﬁnition Language (RDL), as is the ﬂow between ROs and calls to any AOs needed to implement the application. The renderings deﬁned in RDL are made up of high-level visual controls that will be converted to the markup language supported by each speciﬁc client device. RDL Visual controls are similar to HTML tags, but their behavior depends on the capabilities of the target markup language to which they will be translated. Representative examples of these are head, body, title, menu, table, list, etc. An example of visual control is given below. It deﬁnes a table containing the name, address and telephone number of restaurants. ${lrest} references an object in the server context, which is a data warehouse for the mobile application used by the visual controls to pick up their data.

	Name	Address	Telephone

The page style is deﬁned using W-CSS style sheets. A more detailed description of RDL is given in [13]. Fig. 3 shows the result of applying the same RDL rendering description in three diﬀerent devices. The illustrated rendering is part of the ”partner search” use case in a prototype enterprise mobile service.

Fig. 3. Three diﬀerent screenshots for the same RDL

A key component of MorfeoMC is the Code Generation Tool (CGT). This component is responsible for generating the JSP-based servers that will cater for

72

F. Alonso et al.

the client device. The devices are organized according to the supported markup language or technology, and the CGT outputs as many servers as families are supported by MorfeoMC. If MorfeoMC is to support a new technology, a new server should be generated based on that technology. However, some Web content adaption is done at runtime. Runtime content adaption involves data extraction from databases or Content Management Systems, large content pagination or media documents adaptation. This approach to Web content adaptation has been proven to be better, in terms of performance and resource consumption, than others based on run-time transcoding. It has also been proven to be more ﬂexible than XSLT sheets-based transcoding techniques. MorfeoMC source code is distributed under GPL license and is available from the Morfeo project forge [14].

4

Incorporation of Semantic Annotations About Mobile Web Contents

Semantic annotations provide additional information about Web contents so that MorfeoSMC Semantic Web technologies-based content adaptation engine, based on Semantic Web technologies, can make better decisions on content repurposing and personalization than its predecesor in MorfeoMC. This, in turn, (along with the semantic characterization of both the user-interest proﬁle and the navigation context discussed later) leverage the automation of tasks and processes, leading to an added value for mobile applications and services. This includes automatically ﬁlling in personal particulars on forms, rearranging lists/tables of elements according to their relevance for the user, ﬁltering search results that are irrelevant with respect to the user particulars or context, etc. The mobile contents that are generated by the Mobility Channel are deﬁned in RDL. Therefore, RDL has been extended in MorfeoSMC to contain semantic annotations with the aim of describing those mobile contents. It is our aim, however, to preserve the semantics deﬁned in RDL during the content generation and adaptation process (the contents are stored in structured data warehouses, such as databases or content management systems), as well as to incorporate this process into the ﬁnal semantic annotations. Hence, the RDL language has been extended with content descriptions, called semantic bindings, that describe the content that will be ﬁnally sent to the client device. Therefore, the semantic description of the contents associated with the visual controls in RDL can be deﬁned in two ways: (1) writing down the semantic descriptions about the contents to be sent to the client, or (2) deﬁning the source for the annotations of the contents that will be included in the visual control. In both cases the semantic annotations can describe the actual contents that will be displayed to the user and any additional contents that, although not displayed on screen, are useful for adding to the description of the contents displayed in the rendering (e.g. as part of a later layout optimization process as explained in Section 5).

Information Agents for Optimal Repurposing and Personalization

4.1

73

Semantic Extensions to RDL

The RDL grammar is semantically extended by describing the data in any visual control that can contain information in this language. These extensions are based on a ﬁxed set of attributes aimed at specifying the semantic information that will be generated together with the visual control with which they are associated: – about-resid : speciﬁes what identiﬁer one or more resources will take. – about-class: refers to the identiﬁer of the class to which a concept belongs. – about-prop: refers to the identiﬁer of a property, whose value is the data item represented in the visual control. – about-obj-datatype: deﬁnes the data type of an RDF literal, making use of XML-Schema types. It makes sense when the object is a literal. – about-link-prop: references the identiﬁer of a property with a link to the class that contains the data item to be displayed in a column. It is useful for the table visual control. A semantic annotation method has been developed for each visual control that can contain data. In this paper, however, only the Table control, as a representative example of the RDL set of visual controls, and its annotation method will be explained for illustrative purposes. See [15] for a full description of the semantic extensions made to RDL. The RDL Table visual control, used to represent tabular data structures, has been semantically extended with all the semantic attributes described above as follows. The main concept on which the table is based is referenced by the aboutclass attribute, and the resource identiﬁers for the instances are speciﬁed using the about-resid attribute. Each concept attribute expressed in a table column is described by means of: (a) an about-prop attribute if it references an attribute of the main concept of the table, or (b) an about-link-prop attribute if it references an attribute of a diﬀerent concept. Below we show the same example as proposed earlier, now including the proposed semantic annotations and the set of RDF triplets that will be generated as a result of the described annotation method.

	Name	Address	Telephone

Cooperative Information Agents V: 5th International Workshop, CIA 2001, Modena, Italy, September 6-8, 2001, Proceedings

Cooperative Information Agents VI: 6th International Workshop, CIA 2002, Madrid, Spain, September 18 - 20, 2002. Proceedings

Logics in Artificial Intelligence: 10th European Conference, JELIA 2006, Liverpool, UK, September 13-15, 2006, Proceedings

Cooperative Information Agents: First International Workshop, CIA'97, Kiel, Germany, February 26-28, 1997, Proceedings

Cooperative Information Agents VII: 7th International Workshop, CIA 2003, Helsinki, Finland, August 27-29, 2003, Proceedings

Cooperative Information Agents XI: 11th International Workshop, CIA 2007, Delft, The Netherlands, September 19-21, 2007, Proceedings

Entertainment Computing - ICEC 2006: 5th International Conference, Cambridge, UK, September 20-22, 2006, Proceedings

Digital Mammography: 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006, Proceedings

Algorithms in Bioinformatics: 6th International Workshop, WABI 2006, Zurich, Switzerland, September 11-13, 2006, Proceedings

Haptic and Audio Interaction Design: First International Workshop, HAID 2006, Glasgow, UK, August 31 - September 1, 2006, Proceedings

Parameterized and Exact Computation: Second International Workshop, IWPEC 2006, Zürich, Switzerland, September 13-15, 2006, Proceedings

Information Security: 9th International Conference; ISC 2006, Samos Island, Greece, August 30 - September 2, 2006, Proceedings

Cooperative Information Agents VIII, 8 conf., CIA 2004

Cooperative Information Agents XII, 12 conf., CIA 2008

Cooperative Information Agents III: Third International Workshop, CIA'99 Uppsala, Sweden, July 31 - August 2, 1999 Proceedings: No. 3

Secure Data Management: Third VLDB Workshop, SDM 2006, Seoul, Korea, September 10-11, 2006, Proceedings

Cooperative Design, Visualization, and Engineering: Third International Conference, CDVE 2006, Mallorca, Spain, September 17-20, 2006, Proceedings

Advances in Databases and Information Systems: 10th East European Conference, ADBIS 2006, Thessaloniki, Greece, September 3-7, 2006, Proceedings

Learning Classifier Systems: 10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007,

Mathematical Knowledge Management: 5th International Conference, MKM 2006, Wokingham, UK, August 11-12, 2006, Proceedings

Data Integration in the Life Sciences: Third International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006, Proceedings

Cooperative Information Agents IV - The Future of Information Agents in Cyberspace: 4th International Workshop, CIA 2000 Boston, MA, USA, July 7-9, 2000 Proceedings

Cooperative Information Agents V: 5th International Workshop, CIA 2001, Modena, Italy, September 6-8, 2001, Proceedings

Cooperative Information Agents VI: 6th International Workshop, CIA 2002, Madrid, Spain, September 18 - 20, 2002. Proceedings

Logics in Artificial Intelligence: 10th European Conference, JELIA 2006, Liverpool, UK, September 13-15, 2006, Proceedings

Cooperative Information Agents: First International Workshop, CIA'97, Kiel, Germany, February 26-28, 1997, Proceedings

Cooperative Information Agents VII: 7th International Workshop, CIA 2003, Helsinki, Finland, August 27-29, 2003, Proceedings

Cooperative Information Agents XI: 11th International Workshop, CIA 2007, Delft, The Netherlands, September 19-21, 2007, Proceedings

Entertainment Computing - ICEC 2006: 5th International Conference, Cambridge, UK, September 20-22, 2006, Proceedings

Digital Mammography: 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006, Proceedings

Algorithms in Bioinformatics: 6th International Workshop, WABI 2006, Zurich, Switzerland, September 11-13, 2006, Proceedings

Haptic and Audio Interaction Design: First International Workshop, HAID 2006, Glasgow, UK, August 31 - September 1, 2006, Proceedings

Parameterized and Exact Computation: Second International Workshop, IWPEC 2006, Zürich, Switzerland, September 13-15, 2006, Proceedings

Information Security: 9th International Conference; ISC 2006, Samos Island, Greece, August 30 - September 2, 2006, Proceedings

Cooperative Information Agents VIII, 8 conf., CIA 2004

Cooperative Information Agents XII, 12 conf., CIA 2008

Cooperative Information Agents III: Third International Workshop, CIA'99 Uppsala, Sweden, July 31 - August 2, 1999 Proceedings: No. 3

Secure Data Management: Third VLDB Workshop, SDM 2006, Seoul, Korea, September 10-11, 2006, Proceedings

Cooperative Design, Visualization, and Engineering: Third International Conference, CDVE 2006, Mallorca, Spain, September 17-20, 2006, Proceedings

Advances in Databases and Information Systems: 10th East European Conference, ADBIS 2006, Thessaloniki, Greece, September 3-7, 2006, Proceedings

Learning Classifier Systems: 10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007,

Mathematical Knowledge Management: 5th International Conference, MKM 2006, Wokingham, UK, August 11-12, 2006, Proceedings

MacWorld (September 2006)

PC World (September 2006)

Security in Pervasive Computing: Third International Conference, SPC 2006, York, UK, April 18-21, 2006, Proceedings

Data Integration in the Life Sciences: Third International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006, Proceedings

Cooperative Information Agents IV - The Future of Information Agents in Cyberspace: 4th International Workshop, CIA 2000 Boston, MA, USA, July 7-9, 2000 Proceedings

Autonomic Networking: First International IFIP TC6 Conference, AN 2006, Paris, France, September 27-29, 2006, Proceedings

Business Process Management: 4th International Conference, BPM 2006, Vienna, Austria, September 5-7, 2006, Proceedings

Electronic Government: 5th International Conference, EGOV 2006, Krakow, Poland, September 4-8, 2006, Proceedings

Hybrid Metaheuristics: Third International Workshop, HM 2006, Gran Canaria, Spain, October 13-14, 2006, Proceedings

Sequences and Their Applications SETA 2006: 4th International Conference, Beijing, China, September 24-28, 2006, Proceedings

Cooperative Information Agents X: 10th International Workshop, CIA 2006, Edinburgh, UK, September 11-13, 2006, Proceedings

Cooperative Information Agents X: 10th International Workshop, CIA 2006, Edinburgh, UK, September 11-13, 2006, Proceedings

Cooperative Information Agents V: 5th International Workshop, CIA 2001, Modena, Italy, September 6-8, 2001, Proceedings

Cooperative Information Agents VI: 6th International Workshop, CIA 2002, Madrid, Spain, September 18 - 20, 2002. Proceedings

Logics in Artificial Intelligence: 10th European Conference, JELIA 2006, Liverpool, UK, September 13-15, 2006, Proceedings

Cooperative Information Agents: First International Workshop, CIA'97, Kiel, Germany, February 26-28, 1997, Proceedings

Cooperative Information Agents VII: 7th International Workshop, CIA 2003, Helsinki, Finland, August 27-29, 2003, Proceedings

Cooperative Information Agents XI: 11th International Workshop, CIA 2007, Delft, The Netherlands, September 19-21, 2007, Proceedings

Entertainment Computing - ICEC 2006: 5th International Conference, Cambridge, UK, September 20-22, 2006, Proceedings

Digital Mammography: 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006, Proceedings

Algorithms in Bioinformatics: 6th International Workshop, WABI 2006, Zurich, Switzerland, September 11-13, 2006, Proceedings

Haptic and Audio Interaction Design: First International Workshop, HAID 2006, Glasgow, UK, August 31 - September 1, 2006, Proceedings

Parameterized and Exact Computation: Second International Workshop, IWPEC 2006, Zürich, Switzerland, September 13-15, 2006, Proceedings

Information Security: 9th International Conference; ISC 2006, Samos Island, Greece, August 30 - September 2, 2006, Proceedings

Cooperative Information Agents VIII, 8 conf., CIA 2004

Cooperative Information Agents XII, 12 conf., CIA 2008

Cooperative Information Agents III: Third International Workshop, CIA'99 Uppsala, Sweden, July 31 - August 2, 1999 Proceedings: No. 3

Secure Data Management: Third VLDB Workshop, SDM 2006, Seoul, Korea, September 10-11, 2006, Proceedings

Cooperative Design, Visualization, and Engineering: Third International Conference, CDVE 2006, Mallorca, Spain, September 17-20, 2006, Proceedings

Advances in Databases and Information Systems: 10th East European Conference, ADBIS 2006, Thessaloniki, Greece, September 3-7, 2006, Proceedings

Learning Classifier Systems: 10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007,

Mathematical Knowledge Management: 5th International Conference, MKM 2006, Wokingham, UK, August 11-12, 2006, Proceedings

MacWorld (September 2006)

PC World (September 2006)

Security in Pervasive Computing: Third International Conference, SPC 2006, York, UK, April 18-21, 2006, Proceedings

Data Integration in the Life Sciences: Third International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006, Proceedings

Cooperative Information Agents IV - The Future of Information Agents in Cyberspace: 4th International Workshop, CIA 2000 Boston, MA, USA, July 7-9, 2000 Proceedings

Autonomic Networking: First International IFIP TC6 Conference, AN 2006, Paris, France, September 27-29, 2006, Proceedings

Business Process Management: 4th International Conference, BPM 2006, Vienna, Austria, September 5-7, 2006, Proceedings

Electronic Government: 5th International Conference, EGOV 2006, Krakow, Poland, September 4-8, 2006, Proceedings

Hybrid Metaheuristics: Third International Workshop, HM 2006, Gran Canaria, Spain, October 13-14, 2006, Proceedings

Sequences and Their Applications SETA 2006: 4th International Conference, Beijing, China, September 24-28, 2006, Proceedings

Recommend Documents