Artificial Intelligence: A Modern Approach (3rd Edition)

Vice Prc:oi~pb:tn IN rok of learning as wmchng lhe ruch of the ck!itptr 1010 unknown mnronmmb. and •e show hovo' that r...

Author: Stuart Russell | Peter Norvig

12282 downloads 21535 Views 88MB 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

Vice Prc:oi
Protful'iion EdiiOP. Jnne Bormtll ~1\iorOperationsSupervisot: Alan Fisc-her Opcrnaions Sp«-ialist: Lis:a Md)owt"ll M:u'l:cting M.anager. Erin D:wis M:u'l:cting Assistoot: Mock: Patterson Covt":r OcsigllCrs.: K irstcn Sims :and Geoffre)' Cassar Covt":r Images: Stan HoodW<'U)'.libr.u)' o( Congress. NASA. National Mu.scum of Ron'IC. 1\-tc-r Norvig.lan P.1R:cr. S'huttcrstock.1in'IC Lifc/Gcuy Interior Desig.ncn;: Stuart Russell and Pe1cr Nor.•i,g Copy Editor: Mary Lou Nohr An Editor: Greg Dullc:s Mcdi:.a Editor: O:mk:l Sandin Media Project 71-bn:a,;cr: Danicllc lJ.."()r~

For Loy. Gmrlo11, Lucy, George. tmd lstwc - S.J.R. For Kris, Isabella, tmd Julif!l- P.N.

C(tJ))'ri~ltt @ 1010, 200.l. 1995 by Pears(tn EducatiM , ln(':.• Ut)l)er Saddle Rh·er. New Jc ~,· 07458. All rights ~stf\'01. Manufactul\.'d in the United States of Anterica. This public:uion is procccted by Copyriglu atkl pttmissions :.hould bt obtained frotn the publisher ptiot to an)' prohibited ttprocluetiOil, Stot':'lgc in a retrieval system. or tnmsmission in any fonn or by an)' 1neans.. d«tronic. mechanical. photocopyil\g, rerording. or likc-wist. To obt:tin pcrmission(s) 10 usc maactil!IS from this work. plt':mSIIbmit :1 wriuc.n req...estto P<:~rson l-ligher Ed1lcution. P~"nnissions D<:~nmcm. I Llll:c Street. Upper

~ddk

Rh·er. Nl 07453.

or

'l'hc ;auiJwn :and pllbli.shc.r thi.s book h:l\~ used their~ cO'otl.s in prep:t.rin,g lhis bool;. 'rbese efforts indudc Ihe de\'Ciopt'IU'flt, rc$C.Oirth , :tnd IC.'Siin,g the lheocies :uMJ prop-am~ IQdc.tcmlil~ lheir efrecti,·eriCSS. 1'hc author a1M.I publi ~hc.r m:tl:e no w:arr.u11y Qr My kind, expressed ()rf imp lkd. "';th re~;~rd to lhcse pfOi,;aln~ ()r the dOCulnc.nt ~ti()oll cont:.incd in thi8 book. 'l'be :mlhoc and publi-Sher 8hall 1101 be li ~bk in any C\'~'fl l (()r incidental or ~'()o!ISC-qUCntiaJ d.1flla$CS in connt.-ction with, or :arb in$ out of, the fumi.~h ing. pcrf'Om\:un. or u.;c of ll~sc 1>roaranu..

or

l..ibr:~ry (lrC(InJtreM Cat:do~ins.:· in · PubliC"l''tion O:~t:•

on File

Prentice Hall is an imptint of

-

PEARSON

10987654321 IS6N· I 3: 978-Q-13·6Q.I259-4 ISBN-10: 0-13-6Q.I259-7

Preface Arlifkia l lnteUiJ,>cntt (AI) is a big field. :mel this is a big book. We hnve tried to explore t~ full breadth of the field. whic-h cr!Comp.o 't.S••~s logic. pi'Obability. arld coruinuous m:uhem:uies: pcn.ception. rca-.oning. le;tmins. and action: and everything from microelccuonic devict"s to roboti-c r)larlctary exploi'C'I'S. The book is also big because we go imo some depth. l11e subtiUc of this book is "A Mode-m Approach." The intended meaning of this mthcr empty phr.,._o;e is that we h.wt tried ro S)'nlhe.si:r.c wh:u is now knQ\.\•n imo a commorl frnme· woli:. rJthcr than U)'ing to explain each subfi eld of AI in its own histo•'ical ~l tCXt . We apologize to those whose subfid ds arc. a..; a n'Sult. 1e~"'" recognizable.

New to this edition Thi$ edition captures the ch.:mgcs in AI that have taken place since the Ia~ edition in 2003. There have been important af)plications of AI technology. such as the widespre.ad deploy· mc-nt or prncticnl speech recognition, machine tr:.tnsl:ttion, autonomous vehicles., :md hou..'Oe· hold robotics, T here ha\'C been algorithmic landmarks. such as the soluti
Preface

viii

Overview of the book

.nl

m;~~n unif)•"' ~hem~! is the idea o( :an intdliJtf-nl $1Jtt-n1. We defiM AI :1..~ 1k study o( ~b ltult ~h·c: ptt'ttfiU from lht an·it'Oflme'nl and ptrl'orm ac1Klfb. Exh jm..

The

"""'"'

sum

:a Cunt:taon dut ITiilpS pero:pt scquenca lO attiOil<. and •e <'0\et dtfrt'tmt •-.t)"S to ~ 1hnc functions. suet~ as rexch-e .-n•s. ft'.al-tune pbnnm•• and d«bion·lheom:ic ')~C1M. We t>~pb:tn IN rok of learning as wmchng lhe ruch of the ck!itptr 1010 unknown mnronmmb. and •e show hovo' that role consnainti 11en1 dot&n. (a,'Oring t~plic:it ~;;no\\ I· cdet rtpre\.C:ntation and reasoning.. We treat robocks and' •~tOn not t i mdepenck:nlly defined probktn,. btlt aJ. OC'<'Uning in Lhe sen·Ke o(achie,•in.g to:al,, We ''~J.the tmpoctanc'<' of the ta,._ environment in determining 1.he appropriate agent deMs.n. Our primary aim is to cotwey the ideas that have ctm~~~ed O\'Ct the IXIJ.I fifty years of AI R:~;\ttll ;~nd the IX\\1 hVO millennia or n:l:ued work. Wt' h ;~,•e tritd 10 ft\•Oid excxs~ive rorrn:"tl· ity in the pre 'I(: Illation of these ideas while retaining pn.,·chion. Wt' huve inclu
About the Web site aitna . Cl. berk eley . edu.thc Web ~ite for the book.~o.'OfiULini

• tmpkmtl'll;ations or the algorithms in the boo.. in scveml procr.unming l;ang\Qg($. • :1 h\.1 or 0\'Cr 1000 SChoolS lfw ha\'C U$Cd the bool,, lU:&ny With linl,.,. 10 ooJint c:ourse m:.ueri.ali and sylLabi. • an JMOC;~il~ lUI o( m.-n S00 lints to sikS :around lhc Web •11h USC"(ul AI cont
• i&truct.ions on bow 10 romxt the author'\ ~o~rlch qutlitiOib or commtOts.. • irubUC1ions on how 10 rcpon ttroe'\ in the bool..ln the lil.dy event. th:a1 some exist. and • slides and Olbt-r rnatnWs (or 1RSINCtOr\.

About the co,·er postltOR from thr d«-r"~'t game 6 of the 1997 nweh bet•un chess champion Gany K<1S))MO'' and pros.r:.m 0FFP 8 LLE. K aspliO''. playins, Bt.i:.. w~ forte<~ 10 ruign. makin.g 1hii: the fiN lllne 1 rotnpulrr lud beale-n a "'Orid champion rn a c~ match. Kasparov is .!>hcJy.·n a1 the top. To hi~ ltf't is Lhe AsinlO humanoid robol and 10 hi~ right iS 'Thomas 8a)'C'5 (1702·-1761), \loh~ idcnt:: flbOU1 prob.\bility as a nlC3Wrt: or belief underlie nn~eh or modem AI tcchnoiOIY· Otlow 1~1 we seen Mars Explor:uion Ro~r. a robol lhat l:lnded on MaT$ it' 2~ :uul h:1' been exploring the pld>IG
Tbe co'u depicu the final

Acknowledgments This book would noc have been JXh$tbk \lo 1lhout the many contributors ,..hose namt'S dtd not ma.l::e i1 to the CO\'er. Jilendrn Mahk and Onvid Fono)'Lh wrote CNp:er 24 (computer \'iQon) and Sebastian 11uun wrote C~per 2S (robotics), Vibhu Miu~LI wroce pmt of Chaf,tcr 22 (nalumll:mguage). Nick Hay. Mdtr.iJl Sn.hami. :u.d EmeM D;tvis wroce some of 1he eAer'Ci~s. Zornn Durie (George M:.son). 'J'homas C. llcndtf'.wi" in ponic\IIM for hi ~ tirdess ability to ~3d mulliplc dnaft:s wld hdp improve dw: bool. Nid ll:t)' whiptltd the bibliogr.1ph)' .into shape ar'd on de:ulline st.ayc:d up to 5:30AM wrhin1 code 10 makc: the book better. Jon B:moo: (ormaucd and imp«wed lhe dia,gnms in this edt1ton. 'olohde 'lim l-lll:LI'J. Matt PNin. and C)lUhi., Btu)'I\S hclptd wilh daa,;r.uns ~ alpthmi 1n prc:"tious ~ttions.. R:rvi Mohan and Ctanm O'Rtill) •rote :and mait~ain the J:n-.t cc»e e':amptn on the Web site. John Canny the tobolic:s ctup1cr (or lbe fi~ cchtJOn and Ooust).,_s Ed"-atds resc-ouchr:d thC' historical notc:s. Tracy Ounkclbergtr. AlliSOn MK'holel. Scou O.QMO. :and J~ 8onnclJ aa Pt"arson.lntd lhc:u bt" to 1; us on !' htlpful >ug<s~iclns. Most helpful ol all 1m

•"*

Preface been Julie Sussman. P.P.A .• who read C\'er)' ch~pter and provided extensive improveztK".niS. In previous editions w~ hOO proo(rcaders who would tell u.o;; wh~n we left out a com ma and said whidr when we me:uu llrM: Julie told us whc.n we le ft out :1 mi1' US sig.n and s:tid .c, when we mcam :r1 • For every typo or confusing explnn:uion that remains in the book. rest assured that

Preface

xi ~skin. Tony ~sscra. Amit P:.uel. Michael P~.zani . Fcma.ndo PereirJ. J oseph Pc.rta. \Vim Pijls.. Ira l)ohl. Martha l)ollock, David l)oole. Bn1ce Poner. Mnlcolm Prndhan ~ Hill Pringle, Lor· mine Prior. Greg Pro\•an. WiUi:un RajX\pon. Oeep:lk Ra\•ichandi".Ul, Joannis RefMidis. Philip Resnik. F'r~ Rossi, Sam Rowcis. Ric-hard Rus..~ll. Jonathnn Sc-h at.~rfcr. Richard Seher!.

Julie h:l!l fixed :n least fi\•e. She 1>erseveted t!\'t-n when a power failure fon.'Cd her 10 wort by

Hinrich SciiUCIZC. Lar.o ~IUSiet. Ban Selman. Soheil Shams. S1u011 Sh•piro. Jude Sfl.w.

lantem light rather than LCD glow. Stuart would like to thank his J)arems fM their SUJ)pon and eocouragemem ruld his wife. Loy Shc-Oou. for her endk:ss patience and bowld les.~ wisdom. He hopes that Gordon. Lucy. Goorgc. and l s:l.o'IC will soon be reading this book a.fter ti'IC)' haYe forgiven him fM working so long on i.t KUGS (Russell's Unusual Group of Students) h.·we been unusually hc:lpful. :as always. Peter would like to tha nk his p:~rcms (rorsten and Gerda) for gcuing him s1arted, and his wife (Krb:). children (Bella and Juliet). colleagut.·~o:. and friends ror encouraging and 1olcrnting him 1hrough the long hours: of writing and longer hours: of rewriti ng. \Vt> both tll:mk the librarians at Bt-rl:eley. Stanford. and NASA and the developers of Cite-Seer, Wikipcdia, and Coogle, who ha\'e revolutionized the way we do re.._~arch . We can't ;)('.knowledge all the p..-oplt who h.we used the book :.\nd made s ugg<:Siions. but we would like to note the especially helpful commenL;; or Gagan Aggarwal, Eyal Amir. lon Androutsopou· los. KrzyS'ttof Apt. W:.\m.'n Haley Anns trong. Ellel)' Aziel. Jeff Van Baale-n. ~rius Baoon. Bri:ul Buke-r. Shumeet Baluj:a.. Don Barter. Tony Bamu. James Newton Bn.~. Don Beal. Howard Beck. Wo!fgMg Bib<: I. John Binder.l.:tny Bookm:l.n. David R . Boxall. Ronen Bra!· man. Joh11 Bresin:.. Gerhard Brtwl:a. Selmer" Bring.sjord. Carta Brodley. Chris Brown. Emma Brunskill. Wilhelm Burger. Lauren Burta. Carlos Bus.tamnmc. Joao C:tChopo. MuiT'.t)' Cwnpb<:ll. Nonn:ut C'll'\•er. Emmanuel C:astro. AniI Olakrnv:.lrthy. Dan C his.vick. Benhc Choueity. ltoberto Cipolla. D<~V i d Cohen. James Coleman. Julie Aru1 Comp..vini. Corinllll Cortes. Gary Coni'Ctl. EnM:St O;wis. Tom Dean. Rilla Dcdner. Tom Diencrich. Peter Oral:e. C huck Dyer. Doug Edwards. Robert E.gginton. Asm:.\'a EI· Budr.twy. Butbar.t Engelhardt Kutluhan Erol. Oren Etzioni, Hana Filip, Douglas Fisher. Jeffrey Forbes. Ken Ford. Eric Foslcr·lussier, John Fosler. Jen·my Frank. Alex Fr.tn'l.., Bob Futrelle. Marek G all"("ki. Stefan Ge-rberding. Stuart Gill, Sabine Glcsncr. Se1h Golub. Gosta (jrnhnc. Russ Circi1'1Cr, Eric Cirinuon, Bar· bam Grosz. Larry Hall. Ste\'e Hanks. O thar Uans.son. E~t Heinz. Jim l•lendk-r. Chris toph Hc:mn;rnn. Paul Hilfin1:,otr. Robert llolte. Vasant Uon;l\'ar. Tim Huang. Seth Hutchin.wn. Joost l:'tcob, Mark Jelasity, ~'lagnus Joh:msson. 1:-:..tv:m Jonycr.Dan Jurnfsky.leslie Kaclbling, Keiji Kan..,zawa. Surel:ha Kasibhatla. Simon K;tsif. l-ltnry Kautz.. CicnlOt Kerschbaumer. Max Khesin. Ric-hard Kirby. Dan Klein. Kevin Knight. Roland Koeri g. Sven Koenig, D:tphne Koller. Rich Korf. Benjamin Kui1>ers. J:unes: Kurien. John Ufferty. John L.-tird. Gus Lars-son. John l..a7.zaro. Jon LeBlanc. J n...on Leathennan. Frnnk Lee. Jon Lehto. Edward Lim. Phil Long. Pi(:.rn:: LOU\'Caux. Don LO\'eland. SridhM Mahadevan. Tony Mar1cill. J im Ma11in. Andy Mayer. John McCanhy. 0 -;wid McGr.uu•. la)' Mendelsohn. Ris to Miikkulanien. Brian Milch. Steve Minton. Vibhu Minai. Mehtyar Mohri. Leor':l Mo~nstem. Stephen Mugglcton. Kevin Murphy. Ron Mu.o;;kk. Sung Myaeng, Eric Nndcau. Lee Nnish. P:uulu Naynk. Bemhard Nebel. Stu
lik.. Yor.un Singer. StLtind!!r Singh. Danid S leator. David Smilh. Bryan So. Robert Sproull. Lynn Stein. l...'lt'l')' Stephens. Andrc:~s Stolcke. Paul Sttadling. Dc\• ik:~ Subram:t.1lian. M:trek Sud.enek. R ich Sutton. Jon-athlul'l"as.h. Aus tin Tnte. Bas ·n.~rwijn. Olivie-r Tcytaud. Midu1el Thielschcr. Willi:un ThomJ>SOn. Sebastian Thn~n. Erie Tiedemann, M:uk Tomncc. Randall Upham. Paul Utgoff. Peter \•an Beck. Ual Varian. Paulina Varshavskaya. Sunil Vcmuri. Vandi Venn:., Ubbo Visser. Jim W:~ldo . Toby Wals h. Bonnie Webber. Dan Weld. ~\1ichacl Wellm an, Kamin Whitehouse, Michael Dean White, Bri:m Williams , D:wid Wolfe, Jason Wolfe, Bill Woods. Alden Wright. Jay Yag_nik. Mart Yasuda. Rk hard Yen. EHczcr Yud:kowsky. Weixiong Zhttng. Ming Zhoo. Shlomo Zilbcrstein. and our esteemed collesgue Anonymous Re\•iewer.

About the A uthors

1

S1uat1 Ru__~l v.--a.s botn in 1962 in P<wlsm<Milh. En&Jand He: rtUt\"td hts B.A• .., itb fits~· dl.'S honoun &n ph) sacs from Oxford t!nn-n1;a1y m 1982. and htS Ph. D. an compultt samct"

from S1;onfO c( dl< Unn"""Y of C.. ofomiu1 lktl;eley. •hnt' hi: "'• pro(GSOJ o( rompner sc~. dt~ o( the CMkf (Or lnk'lhrmt Systems. and hokkr ol the Smalh-Zadch Chair in En;ginttn"&· In 1990. hi: ~1"-cd lbc- Pttstdenlial

Youn& ln\'C'"Itp!or Award of tbe Na.ioml Sc'iente Found.diOA. and an 1995 he Yfa.1 00\\'iMC'f of d)( ComputeD and Thought Award. He- YiC" on a wide r;,lllt:,~ of topics in artiiieial intelliaen(C. I I i~ other books inClude: 1'1u• Ust• l( Kltc"'ll~/c•tlg~ in A11alqg)' mul hulttetifm and (wid1 [~ric Wc:f:ald) J)tJ tit,. Rigllt Tlting: SttNiit•s /11 Umitcd Rationality.

INTRODUCTION

•o

/11

g
We call ourseh·cs Homo SiiJJirns-mnn the wi~bccau!'e our intemgena.> is so impor~ana to us. For thousands or rea~. we have tried to ualdrn.tand IN1w wc think: th:u is., how a mcre tw.dful or mauer can pcrcth·c. undrNMWI, pred11:1. and m.a.nipul:uc .a world rar largtr and rnon' complicated thm itself. 1be field or ar1iRdaJ lntdliJtmtt. or AI. goes (unhrt- s1lll: tl auemps noc just 10 ~ bul al..o co OOtid •n~c ll i~ff~t erni1ies. AI is Olllt" of W newest fiekls 1n ~tt'ftC'C and ~ncatnJ.. Woct suncd in c:t.mt'SI soon afitf Wortd War II. and lhe name ibetf "~)- C'Oincd a.n 1956. Along " 'ilh molc'ICul.- bioloay. Airs rqubrfy cited as lhc -Midi \\OUid moM hk 10 ~ Ul.. b)' 5CK':nhs&s in otberdlSC'aphnes.. A quc~en, ;n physM:s miJ!w . . . - . , feelllul olllh< Jood ;dea< ~>a>-. ol=ody bmlul;m b)
l•tetr Nur•·iJot i~ currmtly Director of R~arch :u Coogk, Inc.• and w.t!i thr director rt-.spon· Mbl(: for lhc core Web se:areh algorithm$ from 2002 to 2005. ltc a.; :a Fellow or the Americ» A"«laaion (or Arti6ciallntelli_gen« and t~ Ati...~i:uion for Computin5 Machinery. Previou,Jy. he "~ hc.w:l of thr Compu101tional Scienct"~ Di,•i~ion :u NASA Amc-' RCSC".vdl Center. "ht-re he 0\t"NW NASA's teS('.arc:h and de\"tiopm.MI in 1111tiC'ial uncll•l~ and robotics. and dud !OiC'.mllSI at Jungl«:. whett he hetptd de\·etop ont o( lhc' fiN lntcmd tnforrautioo niiX'bOn ~" ac«. He t"CCCi\~ a B.S. in appr!Cd ~~ from B~'ft 1Jfti\'trSiry and a l'tt 0 . "' COinpuk:r 5C1tQC'C' from the l!ni,-nstry ot Callfom1a a1 Snlclq. Uc l"tttl\cd lhe Dll-llft.IUI~ Alumna and ~ lnni)\..Vion a..,.'JRI)- (rom B~k) and the Exct-p:ional Achlt,'C'mcnt Medal from NASA. He has brnl a p-of~ at the Unl\'f1"SJI)' of Soulbcm Calafonu.. :md a 1'\"SC<J~rth fxu lty mcmbct :u 8ntc-lcy. "'" ot.hc-r bed.'I :arr Pt•rud;gnu of AI Pmgnunntmg: CiUt' Stlldit's in Coou111on Lisp and ~'c>rbmobll: J' 1'nmJiatiQII Syslt',ll/or FoultJ·F'ttct' Dhtlog :uld lmtlligtm Hclp Syst~ms for UNIX.

I.I

~

x:ii

"·hich Wt' II)' llJ t'.fplm'n '•'h)' h 't' etmJitlt•r lll'llfitiul intt'lfl'gf'nCt' 10 be tt s11bj«t

lti0$1 WOI'Ih)' tJ/Sltt(/)'. muf/11 whirh W4" try ltJ i/t>t'/t/(' •I IIIII t'XII("t/y it iS.. tltis f>eiltg tt

WHAT IS A I? We t.J,·e claimed lh:il AI is C'Aching. but "c h:we 1KM ~a~ " 'hat it is. In Figure I. I we~ dghl defini tions or AI. l:.id Qut alone 1....-o dlnk'n!liOn!l. 1'hr: cktini1ions on 1op are concerned ~ith tl•ought prot:~~Ms and fMj~fi'IJl· "hcfea, the ont( on 1hc: bot-tom address bt'hu~·;or. Tbe defimltonS on the left me:asu~ suooeu an ltmh or fi
id""'

2

--

Chl'I)IC'r

I.

Scc1ion 1.1.

ThlnkinJt Humanly

Thinldn~ Ratlona.JI) '

"1llc C'}.Cihn& At\\' dfon to mate e<wnpu•~ m than&. ••• maduMs •·iJJt minds. in 1M

1br "udy of mmt&l f~n~h~ dvougb the

full and h..,.. ........- (HouldancJ. 1985)

1

o« of
..urn

""'Tht \lud) o( 1M ('OIUpuul 110M f.hac nW;c ic potS)Ibk to pc-~tt\'t, reason. and aa... (Win~aon. 1992)

AclinJC Hunmnly

1\ clln~ R11tionully

'"ITbt

:.UiomaiKJn ot) anivities thai we

~~•Me

"1th human thinking. acth·itic's as dtt•:)aora.rtWdng. problem solvma. lc;~ming . ...• (6ellm;m, 1978)

or

'1'br- :u1 &.-rcaling ll13Chincs lhat per· fonn fu tKtiOn.$ that n..-quirt intelligt•nct" when J>erfontk.'d by p..~>k." ( Kunweil.

-.. ww.,...

•

('OO)puter 'ision co pm:ci\·e obj«h, and

liiC*IhCa

•

robotic$ 10 mmipul:.ale objc'C1~ and ll'IO\'"t" about

These six disciplines cornJX* ~ o( A I. :and Turin&
N 11/., t99R)

t~

..AI ... i.: concemcd wi1h imclli&;ent be· h.·wior in lll1ifetcLt."' (Nd!ro~n . 1998)

1ft mote' dC"C;aal,

1.1.1 Acting huma nly: The Turing Test approacInJ \CMM writ1en quC"S.~ions.. cannot tell whetha the wrinen "''IXIn~s come from a person or from .1co.uputt:r. Chaptt-r 26 discuS:5Cs the cktai l~ or the IC!II and whcd'M:I' :acomputer v.wld rt~lly be: intelligent ir it pass~.'(!. For 1\0\lo', we note thlll pn)8r.tmmina a computer 10 pa'iS a rigot04hly :llll>hcd ttS-t I)C'Ovides l'lenty to work on. ·n,c t.-orntJutcr wouk11~d 10 phSCSS the following c:~t,abilitic~":

~lAIICl.II{M

• mtturallanguuge processing to enable it to communic:~te \UCce...~fully i11 E1 lglis.h: • kntw. h.'Cige N"pmtnlation 10 Slott wh111 it t..now., or hcnl'l:

=

•

...c.~ UMflrlfiO

•

~

automnlt.'CI n.>nsoning to use the StOftd information to :Ul\\\'Cf queMions and 10 draw new cone! usians: rna('hlnt' lea mJng 10 adap 10 Ae\\' cirtum.~mct" and to dd«1 and eMrapola1e pauerM.

8) . "......... kt-.u. ....._. aiJd ,.,.,..., ~. '&'(" I t t 1101 W!Qr". . lhlil ~ art ~Ja'ily waoot . ..~ ~- . ""--t - 0. Mnrl) M'ftl . . . . . . -art 0011 pnfccc .... • chr<;\ ~"""- P , 'm:-. ~ , -~u,.... A s-nNn • . . . . _ . _ , . . . . a.r nulofcd..,. K..__ ,,_ C l91!~ I

........,.,. •

em• ..-.-n-..

,-....c

making ..machines th;tt fly M) ex:u.11y m.c t)igcon.\ th~1 1 lhty can fool t'\-en other l'igeons."

1.1.2 Thinking humanly: The cognith·c modeling approach

\'Oh l"J ~n
1Uiltl61ftr

1\arin.g·s ICSI deliber.uely a\"Oidcd dirm ph)"IC#II inlctaC'ItOn be1ween lhe irUerro£aiOr and the computer. beca~ ph)'firal s•mul-'11011 of • pn'On ~~(or inlelli~. HO\\·to.-e.-, 1ht so-a.Ued total Thring THI intluck:... a \ Ideo ~nal so thai 1.he in&c110piCll' can k':W thr .WbJC'CfS pt"rt"C'plU3i abifibtS., t i 'ACtl t i lhe: oppot1URIIY (or tbr tniC"fl''PPCW tO ~ ph)'SICal

CCM'\I!WI_.

"Computati or~;~ l lntclli,JCI'K'C i~ 1he s1udy o( the de:.i£_11 of imelligc:nt agc:IILO:," (Poole

hc-tpt'd C'-Xh ocher. Ld YS Joot at the four appro;k"hcs

3

abJOOIS "lllnlugb die llolttl." To pm lhr tocoll\ntnt Tt
1990) ·11~ ~tud y of how 10 mttl:e compule18 do thill$lllll wh ich. al lhe moment people a~ ~u..-r." (Rich and K1,ighl. 1991)

Whatls AI?

COQN:n'ltiC:I9Cl

If we are ~oing to say thai a gh·en progr.un ttunt..~ ltt..e a humllll, we musa h.a\"e some way of delennining ~· humans think. \\'e ~ 10 ~ iiUitk lhe accual w'Of'tings or human minch.. There aR: th~ •'a)"SIO 00 lhi!t: tlvou&h 1nt~tOO-tr)in,g 10 eaac:h ourO\\n thoughb I) dte) go by.lhn>ulh ps)...-gi
bsgram·,. UlpUI-outpW t:w:ba\-ior m~ corn•,pc,nchna: human bctQ\·tor. ltw is cvic:lencc- dul some o( thr P"'Onnt':s ~isms could abo be opc-r.ahnJ an hum:an.s. Forc:x.:unpk. Alkn Ne'i''tll and Herbert S i:man. • ho ck~tcloped Ci PS. 1he "~n.:nl Pwlbkm Soh'tr- (NCYt~IJ :and S inKln. 1961). Wf.!':re not con1en1 meetly 10 h;lvc 1ht1r prov.uu :soh·e problems COI'Ttt'tly. Tbey were: nM"ll't concerned with comparing the lr.tec or 1h ft,lj()llll\.1 Me~ 10 tr.aces of human subj«'b solving 1he same problems. The intC'nli\oCi iJhn~ry tickl o( t:ognilh·e scien ce brings togedw.-r computt-r models from AI n.nd cx~rim~·~u.-ltL"("hnique.<~ from J)$)'<:hology to con.\lruc t t>~dse <'nd It-stable tlK."'rics of the hurn:m mirKI. Cognitive science is n rascin111ing field in it:r.cl(. woohy of se\'cml tcxtbool:s :md at h::N o.•e ei'IC)'ciOj)t(fia (WI bon 'll <»anilion. Keul oognilive science, hO\\'C\'Cr, is r~eceswil y lxl."'C'd on exptrimC"nliLI iiWC'\tiS"'tion of ntclual hum.ans or animals. We w-illle;~~,•c thai for ott\(:r books_ as "-e as..<;;ume the n:oader ha!> only a computer (or aperimencation. In lbe early cbys of AI thtfto wa!> often ('OnfuQon bet\\een the approac:~ an a.u.1hor would arpae thaJ an :~lgorithm pffl"omH; 'ill til on a ta.\.k and U\31 il is tlJurfo" a good nlOdtl o{ human perfonnance. (){ \ice \'f'r'a. Modem a~hon q-pan~c lhe IWO kinds or c:bim~ this daslindion has aUov.-C'd both AI and cop1tr\'C SCKIIIX &o <11:\'C'"Iop ~ r:~pidly. 1bc 1'1.'0 lk:kls rontinut- 10 (eniliu c.xh othrr. mosc ncnbly m computer \-woo. -.JUch incorpontt":S neurophysiologiea.l to.idenc'c' inco Cl(>tnpul;ahoruJ ntOCklt.

Chapter

4

I,

Introduction

Section 1.2.

1.1.3 Thinking rationaiJy: The ';laws of thought'' approach

th<'lt :dways yielded correct conclusions wher) given 00mx1 pl'tmises- for ex:unple. "SocrJtes mt'll

are monal: therefore. Sornuc.•s is monal." These laws of thoog_ht were

su,,pose
'"'"'"

Logicians in the 19th crotury developed a precise notation (or stalt'tnents aboot all kind..; of objects in 1he world and the rel:uions :1.mong 1hem. (Comro,~tthis with ordinary arithme1ie notation. which provides only fo-r statc.·nu':nts aboul mmrbus.) By 196S. prog.r.uns cxisled th:l.t could. in principle, solve atty soh,able problem dc...~ribed in logical n01mion. (Ahhoogh if no solurion exis.rs, rhc pR.>gr.un might loop forc\·er.) 1be so-called logicisl rrndirion wirhin anifici:tl inlelligcnce hopes 10 build on soch progi"J.m.s to t'rc:tte inrclligcnl s~tem.;:. There arc two main obstacles to this approach. First, ir is nor easy to rake infonnal knowll-dge and state it in the fonnal tt"mls required by logical notation. particular!)' when t~ knmvlcdge is less !han 100% certain. Second. !he~ i.s a big diffe~nce bcrwccn solving a J)r'Obk·m " in principle"' and soh•ing i1 in J)r.lCtict-. Evtn problems wilh jus! a few hundred facls can cxhau.;:t !he computational re.<:ourcc.s of any computer unlcs.<; it has some guidance as 10 which re;~sOtting s1c.ps 10 cry first. Allhough both or these obstacles apply to (Ill)' auempt 10 build comptnmional reasoning syslt"ms.. !hey appeared first in !he log_icist trndirion.

1.1.4 /\ding n1tiom1lly: The ratiom1l agent approach An agent is jus! something lhat acL.; (ag~m comes from the Latin IIJ;ere. to do). Of course. :.\11 COOlJ)UICt progtJms do rornerhi11g. but computer agaus arc expected 10 do more: opernre aulonomousty. pc-rcdve their t"cwironmcm. JX"tsist O\"« a prolonged time JX"riod. adapt to ch:mgc. :.\lld create and pun.1.1c goals. A mlional agtnt is one th:tt acts so as 10 achieve the best outcome or. wbl.~n there is uncx·rtainly. the best expc.·cted outcome. In chc "laws or though!'' approach 10 AI, 1he emphasis was on C()t"rl!CI inferences. Mak· ing com."<:t infert·nccs L.;; somctimt.-s JNII1 of being a mtional agent because one w;1y to net r.uionally is tOreason logically to the conclusion !hat a given action will achieve one's goals and then to act on that conclusion. On the other h:ukl. correct infe-rcnce is not all or mtion· ality: in some situations. there is no provably COITi."Ct thing 10 do. bur somtthing mu.;:l still be done. Then: are also way.s of :tcling r.uionally that C:tm\01 be said to invol-..•c inference. For example. te<Joilil\g fr~>m a hot stove is a renex. acrion that is usually man:.: suc<:essful th."lll a slower aclion l.n.kcn af\er careful dclibcralion. All the skills needed for the Turing Test :\ISO 311ow :.ln agen110 :·tct r.ni on~Jiy. Knowledge rcp~sentation and ~ason ing enable ngcn1s to rex-It good decisions. We nc.~ 10 be able to gcncm1e compre-hensible sc::nrcnccs i.n natur.d language 10 get by in a complex. sociel)'. We need k iLming no1 only for erudition. bur also bc.•cau.sc it improves our ability to gencr.ue effective behavior. 'rile mrional-agenl approach ha..; two ad\'antag_es over the oth(~r approaches. 1-irsl. it is more gt"ttcral than lhe ..laws of thought" approach bec-ause COITCCI infercnc.-e is just one of sc,·er:d ~iblc mech.:l.nisms for achieving r.llionality. Se-cockl. it is lllOI'C amenable tO

s

scientitie development than are :I.J)pn>3c.hes bil.Std on hum:~n beh ~vior or human thought. The $-landard of rmionalil)' is marhenuuic:dly well defined and eomplctely ,general. and can b: "unJXtC-kod" to gtltcl".lle :agent designs thou provably ~hie\'e il. Hurnan bch;wior. on the ocher hnnd. i.<> wdl :klapted for one spc.•·cifie e-nvironment and is defined by. well. the sum tocal of Ull the things that hum:u~ do. This baok tllt•rejo!'f t:m~t·mmtt'S Otl l]em•ml JJtinti,JII!s of mtimwl ag~nts and 011 mmpo11em.c jtlr cmutructing tlt~m. We will see that despite the ~pparent simplicity with which the problem e:~n be Stated, an cnonnous ' 'ariety of issues come up when we try lo solve it. Chapter 2 outlint.•s some oftbi."Se issuc."S in more derail. One impor1ant poimto k-eep in mind: We will see bcfon:.: too long that achieving pctfect mtionality-always doing the right thing-is not feasible in c.-omplk;1tcd t."'tvironrocnts. The c01np..ua1ional dem ~nds :-tn: just 100 high. For most or 1hc book-. howc\·er, we will adopt the 'llity it' a good st~t1ing poim foranaJysis. It simplifies working h)'pc>thesis th~t perfect r:.uiono the problem and pnwidcs the ;!ppropriate setting for most of the found3tional ma1cri:tl in the field. ChapletS S and 17 deal expficirly with the issue of limited rationality-acting <'PJ>I'Opri;ttely when there is noc enough time to do all 1he compu l~tioni> Olte might like.

The Gn."<:k ~•ii()S()pher AriSIOlle wasorle oflhe fi rst to attempt to codify "righltllinkin,g." that is. irrefutable reasoning processes. His syllog.i'ims provided p."'ncms for argument s:tmctures is a mnn: all

The Nxmdations of ArtiliciallnteUige.nce

1.2

THE FOUNDATIONS OF ARTIFICIAL I NTELLIGENCE

In this se<:1ion. we pro\• ide a bricfhisrory of the disciplines th.:u coruribotcd ide#. " iewpoims.. and techniques to At. Like any histOC)'. !his one is fO«'OO 10 concenlrnte on a small number or people. cvtniS. and ideas and 10 ig1101'C othei'S that also wert impor1a:nr. We orga:ni1.e the hislocy around a series of questions. We ccl1ainly would no1 wish to give lite impn-ssion th3t these qucsti011s are the 011ly ones the disciplines address or that the disciplines have all been wori:ing toward AI as lhd r ultimare fruilion.

1.2.1 Philosophy • • • •

Can rorrna.l rules be used 10 drow valid conclusions? How docs lhe mind arise from a physical brnin'! Where docs knowledge come from'! How docs knowledge lead 10 action'!

Arisrotlc (384-322 B.C.). whose bus.t appca.rs on the fron1 cover of this book, was !he llrs1 to fonnuJate a. precise set of laws go\·e-n1ing the rational part of the mind. l-Ie de\'e-loped an infom1nl sys1cm of syllogism.." ·n11: automarion of computarion itself was already well WlCltr way. Around 1500. l.eonal'do do'l Vinci (1452- 1519) designed bur did not build a me· chnn.ica.J calculalor: 1\."«f
Chapter

6

I.

Introduction

is more f~lOUS. ~seal wn)(e th:tt "ll.e arithmeti<:al mx hine produces e ffcciS which ~PI>eat nC3rcr to thought than al1 1he actions of animaL-.:· Gottfried Wilhelm Leibniz ( 1646-17 16) buill a mcc.h:uti<:al de' •i«: inte•'lj>er.uions (Ul <:01'1Cepts r.nhcr th.:m numbers. btn iL;; scope wa..<> rotht.'t limited. Leibniz did surpass P:LSCal by building a calcul:uor that cool ~

odd.
tract. Some speculau.•d that machines might not just do culculations but actually be able to think Md act on their own. In his 1651 book Ln·imlum. Thomas Hobbes suggcs.tcd the ide:'~ of an ..artificial animal." arguing ..For what is the hc·art but a spring; and the nervt·s. but so

-

"'"" """""'

many Sltings.; and the joints. OOt so many wheels." ll's one thing to say that the mind opcmtcs. atleas-1 in part. a~.:cording to logical ruks. and to build physical systems ltt3t ernul:nc some of those n1lcs: il's anochcr tOsay 1h:n 1hc mind itself is such a physkal system, Rene Dc..~ancs (1596-1650) ga"c 1hc fi rsa clear discussion of the distir)(:'liOn between mind nnd lll:;'IW:-r and of the probkms that arise. One problem with a purely physical concep!ion of the mind is that it seems to leave linlc room for free will: if the mind is go"ent«< Clltirely by t>hysical laws. then it has no n.ote fR-e will than a rock ''deciding·· 10 fall loward the center of the eanh. Dcsc:trtes was a strong ad\'ocate ofthe power of reasoning in urKie-rstanding the wor'ld. a phii()S(Iphy now called n-.lionalism. and one that counts Aristotle and Lcibnitz as members. But Ocscnnes wa.-. also a proponent of dualism. He held th.at then: is a p:_ln of the hum:trl mind (or SO\II or Sl)iril) that is outs-ide of rl:l.lurt. exempt from physical laws. Animals. on the otl•er hand. did not possess thi." dual qu:ality: !hey could be treated as m:.chi r~s. An ahcm;uive to du:dism is materialism. which holds th:u the bt'ain 's oper.uion according 10 the laws of physics cm•stifllfts the mind. Fn..--e will is simply the way lhatthc pt."Tttption of available choices a ppt'ars to the c.· hoosing c.•ntity. Give•~ a lm)•sical mind !h.'lt mM ipulatcs kn()Wk-dge. the next problem is 10 es1abtish the soun."t' of knowledge. TfKo t:mpiricism movc.'lm~nt. stal1.ing_ wilh J::'nmds Bacon's (15611626) N0\'11111 OI'IJ(mum.~ ischa1'3Cicrized by a dicuun of John Locke (1632- 1704): .. No1hing is in the undeNtanding. which was 1101 fi rst in the scn....·s:· David l-lurnc's ( 17 11- 1176) A Ttt•<Jtitt: (If Htmum Noturr (Hurnc, 1739) proposed whar is now known as 1hc principle of induction: that !,"t-ner.ll rule.-. are acquin.-d by cxp<~~:5u re to repe;ued associ;uion.s between their clem ents. Building on the worl: of Ludwig Wingcnstcin (1889-195 1) and Bertrand Russell (l872-1970). the f;1m0us Vicrma Circlc. ll-d by RudolfC.amap (1891- 1970). de\"CIOped the doctrine of logical posithism. T his doctrine holds that all knowledge c:ul be char.:~cterized by logical cheorics connected, ullimately. to obst:n•ation st:nte.nce$ time correspond 10 se-nsory inputs-: thus logical pOSiti\•ism combines rJtionalism ;uw,1 empiricisrn.3 The C"Onflnnation tht-· Or)' of Camnp and Carl Hempel ( 1905-1997) auemp1cd 10 analyze the scquisition of knowI· edge from experience. C:.rn~p·s b<'>ok 11rt' LtJglcal SlmC'tml' of til;: World (1928) defined an explic-it con1puuuional procedure for extracting knowk"<fds. 8«-JtUSC this rul~ out mos• ol ~~~pll~ic$, t i "'~the imC'I'Ic.ion. tot~ ~c.ivism w~ ~popuiJt iQ son~ .:in:k$.

Sc<:1ion 1.2.

The Nxmdations of Anilk iallntellige.nce

7

l11e fin al e lcmeru in the philosophical pie1ure of the mirKI is the connc<:1ion between knowledge and 3Ciion. 1'his question is viud to AI because intclligcOCX' requires aC'Iion as well as reasoning. Moreover. Octly by understanding how actions are justified can we unders1a1~d how to build :m agent whose :ac1ions arc juslitiablc (or mtionn.l). Ari...-tocle :ugued (in De Motu Animt,limn) thai actions are juslified by :l logic:tl COtuM!<.'t ion bclween goals :uld knowledge of the action's outcome (the hL.;i p:art of this extr.tcl also appears on the front CO\'e r o( this book. in the origi•l;d Greek): Bu1hC.)'4'«Joel! il h:.&ppcn U\0\t thinldng i~ l!OflWiimell <~«Ofnpanied b)' 11etion an d son-.ecimes not, $(1fnelinws by motion. ;md ~nw:cimes nor? II lool:s ••:s if almosl lhc s:JnW: thin£ happen:s :•.s in the case of n:"sonin£ :and m:Udng infen:-nc:~o."$ about unc:h
cloo.k, Wh;al I noed, l

l~e

10 m:d:e;

I IIC'td a d~1k , I h:.-ve IOm:1tc a C!.c.):tk. And

the c:onelu!lion, the ..1 h.:wt to n~kt a do:ll:,-l$ :111 ac1ion.

Ln the Nin.,machC'lm Ethics (Book lll. 3. 1112b). AriStOIIe further clabor.J.Ie.s on this tOpie. suggeSiing an a lgorithm: Wt dclibcr.atc II()( about ends. but about means. For a doc1or doe~ not
1.2.2 Mathematics • Wlmt Me the fom•al rules to dr.Jw ~lid <:or-elusions? • What can be computc.'d'! • How do we reason with unccnain infomlation? Philosophers staked out some of till: fundamental i
Chapter

8

I.

Introduction

Section 1.2.

worked out the details of propositional. or Bool~:.a.n. log.ic (Boole. 1847). In 1879. Gonlob F'rcgc (1848--1925) extended Boote's logic to include objecls and rcl:uions. crcatinglhc llrst-

t'.~'tmm!ly

The nex1srep w!lS 10 derennine lhe limits or what could be dor1e with logic and com.

=:tOIUS

~

~

~

putation. 1'bc first nocuriviiLI algorithm is thought to be Euclid's algorithm for computing greatest commo-n dh•isors. The \lo'Ord (J/gorirhm (a1ld the idea of smdying them) comes from ai-Khowarazmi. a Persian mathem::uician of the 9th century. whose writinv also introduced Anbic numerals :.nd aJ~ebm to Europe. Soole and others discussed algorithms for logic:d deduction. and. by the late 19th c.:v:ntury. efforts were under way to fonnalize g,cncmlmathe· m:uical reasoning as logical deducti<>n. In 1930. Kun GUdcl (1906-1978) s.hO\\'ed th:u 1herc exists an cffecli\'e procedure 10 pro\'e any true s.tatcment in 1he tir:st-ordcr logic of Frege and Russell. but that fi f'S-1-order logic could not c-apture the principle of m;ltht.·matie-;LI induction needed to eharncleri7.c 1he n:uurnl numbcr:s.. In 193 1, GOdel showed d'-"t limits on deduction do exist His int(Hnpltteness lhoortm shov.-ed that in My fonnal theory as strong ~ l,cano :trithmelic (the elementary theory of naturnl numbers), there :tre 1 n~e saatements I hat are undecidable in the sense that they h:we no proof within the theory. This fundamenta l result can also be inte-rpreted a'i !iihowing that some function." on the irnegers c;mnoc be represented by an ~lgorithm-lhat is. they cannot be computed. T his moti\'ated Alan Thring (1912-1954) to II)' to c-harnctcri:r.c cxnctly which functions are com· putablt-c:J.t,able of b<:ing COII)I)uted. This notion is ~t ually slightly problematic bec-ause the notion of a COII)Jn•t:uion oreffecti\·e ,,rocedure really caJu)()C be given a fonnal definition. How•.wer. the Church-Turing thesis. which staiC.'.S that the Turing m<1chine (Turing. 1936) is CaJ);)ble ofcomputing any computable function. is gener.llly ..ccepttd <'S J)I'O\'iditl.g;) sufficient definition. Turing abo showed that there were some functions that no Turing m:K'hine can C()fnptnc. For exampk, no m.'lChine can tell ;, gen(rn! whed'ICr a given ptog.r.,un will retum an answer on a gh·cn input or run forever. Ahhough dccidability and comptu:tbility arc imponam to an unders1anding of comJ>tl1:l· tion. the nocion of tract.abilily ha.o; had an evc.·n greater impact. Roughly speaking. a problem is called imrnctable if the time n..'(Juin..'
F~ '$ proposed

llOIIltion for

llC\'ff b«lln'IC popul•.

li~t.oo.k:r SQs.ic-an wcane combi~tion

of tc:u~l :u.cl gCOO'IC1ric

9

problems are necessarily intr.l(:table. most theoretici:l.ns believe it) TI)C:Se results oorur..st with Lhe optimism wilh which che popular pre-ss greett.-d chc first computcrs-.. Ekccrooic Supcr-Br:.lins" that were "Fascer than Einstein!" De-.spite the i1'M.!-reasing speed of co-mputci'S. careful use of resources will char:K'Icrizc intelligcnl systems. Put crudely. the world is an

on:Jer logic th:u is U-sed tod~y... Alfred TMSki ( 1902- 1983) introduced a theory of refereoce th.'lt shows how to relate the objects in a logic to objlocts in the real world. Jt(qiiMII

The Nxmdations of Anific iallnteUige.nce

large problem in!l:l:lnee! WOtk in AI h.'l.S helped

expl :~ill

why some insranees: of

Nl>-completc problems nre hard. yet others arc easy (Cheeseman ~~ al.. 19')1). Besides logic and COO)J)Utation. the third grca1e<~n ttibution of mathern:uics 10 AI is 1he theory of probabiJhy. 'l'he llrob:tbility quickly bec:une M invaht.o with uncc11ain measurements and incom))k te thoorit·~"· Jameslkmoulli ( 1654-1 705). Pierre LaJ>Iace (1749-1827), and others advrul<.'Cd the theory :tnd introduced new s.latislical meth· ods.. Thoma..; B:t)"eS ( 1702-1761). who aPJ>e-aN on the front cover of th_is book. proposed :t rule for upchuin.g prob:tbilities in chc light of l'k!W evidence. Bayes· n1le underlies most modem appi'():'IChes to uncertain re~soning in AI systems.

1.2.3

Economics

• How should we m.ake decisions so as to m:v:imize 1,ayoff! • How should we do this when olhers may not go along'? • ~low should we do this when the payoff ma)' be (ar in the fu ture'?

OEOSIONn«::llrr

f<mu.~

Glo'lf:M<:Ifrf

The science of CC<~nomics got its S.l:l.n i1) ITI6. wh<'n Scouish philosophtt Adam Smith (1123-1790) published Att lttq11iry into tlu! N(llllfr ant/ Ca11.ses iif tlu! U't'alth of Natiims. While the anciem Greeks and others had made contributiQI)S IOe<'OilOmic 1hough1. Smith was the first to treat it as " scit.·ncc. using the idea that economics can be thought of as consist· ing of indi"idu:tl a~ts maximizing their own economic well·heing. M<JS~ people think of economic:~; a..; bci1\g ;tbout money. but t.'Conomists will say that they are n":\\ly studying how pcOJ>Ie mal:e choices that lead 10 pn:fen-cd outoontcs. When McDonald's o ffers a hamburger ((K'a dollar. they are a$$trting that they would ))refer tl'le dollar ;md hoping thai c ustomers will J)refer tl'le hamburger. T he mathem:uical treatment of "prefern.-d ouccome..;·· or utility ' v:lii first fonnalii'.ed by Uon W-ttlms (pronCMu1ced "Valrnsse'') ( 1834· 1910) and was impro\'ed by Fmnk Ramsey (193 1) and latt.r by John von Neumann and Osk:u- Morgensten• in the-ir book 11te 'fliMI)' of Gtmu~s am/ Economic Belw\'l·or ( 1944). Decision choory. wl1ic.h combines probability thoory with utility theory. ptovides a for· mal :uKt complelc frnmework for decisions (economic orochcrwisc) m:Kte Ulldcr uncert.aintyth.1.t is. in cases where: l,rob.,bilistic descrip1ions :.ppropriately capmre the decision maker's environmt.~nt. This is suitable for ··targe" economics where encb agent mocd pay no anention to the ;)C'I ions of ocher :\gentS as iHdividu:lls.. For "small" economies. the situation is much more like a gam e: 1he actions of one pl3)-cr can significantly affect the utility of unocher (eilher positively or ncg<~livc l y). Von Neumann and Morgen.~u~m·s development of grun t theory (see also L.ucc and Raiffa. 1957) included 1he surprising result !hal. for some games.

10

Chapter

I.

Introduction

Scc1ion 1.2.

The Nxmdations of Anilkial lntellige.nce

II

a r.\tional agent should adOJ)I policies 1hat are (or leas:t :.'lppear to be) randomized. Unlike d¢.. cision theory. g:u~ lht'OC)' docs noc offer :m urwnbiguous prescription for selecting actions. For the m0$t pan. eronomiSis did noc add~.ss the third queSlicm listed above. o.'\mely. how to make r.uionaJ ckcisioo.s when payoffs from nclions arc not immediate but in:<:u.•:td re·

su11 from w•er:d acrions raken i11 srqm!'u•e. This IOJ)ic was pur.rued inlhc fieldof operarions n.•St.oan-h. which cnx'Tged in World War II from cffons in Britain to optimize radar in~all a·

tioos. and later round civilian applic-:uions in complex m:magemetu decisions. The wori: of ltichatd Bellman (19S7) fMrn alizc.-d a t•lass of sequential decision pcoblcms called MarkO\' decision J)r'OCessts. which we SIUd)' in Chapcers 17 and 2 1. Work in c.··conomk~.; and operations research has t."'ntributcd muc:h to our notion of r.•· tiona! agcms. )'CI for many )~t'S AI research dc\'e)opcd along cmircly separntc paths. One reason ,...,s the :tl>patcnt complexity of making rational d~isions. ·n,e pioneering AI I"C'S<'ar<:her Ucrben Silnon ( 1916-2001) won the Nobel Prize in l-"COnomic,;; in 1978 for hb: early work showing that models based on .satisficing- making decisions that arc "good enough," rather than laboriously calculating out op~imal decision-gave a bener c.'k.scriptiOn of actual hum:m bch:lvior (Simon. 1947). Since the 1990s.. there has been n resurgence of interest in decision-ll)C(H"(."tic tcchniqm·s for agent systcn\S (Wellman. 1995).

1.2.4 Neuroscience • How do brains process i.nfonnati.on'? Ncumscic.ncc i..; the sllKiy of the Oi."I'\'OUS system. particularly the bmin. Although the exact way in whi-ch the bt.lin enables thought i.s OEle of the gte-:u mysteries of science. the fact thou it dt~s enable thought has lx."Cn appn•ciatod for thousands of yt"lU'S because of the '-"'' idence that su·oog blows to the lle.'ld ca11 lead 10 me•ual inca~citation. It has also long been kn()W'n that human brains are somehow diffcn·nt: in about 33S B.C. Aristotle wrote. "Of all the animals. man h.-,s the largest br.a.in in proportion to his si7'.e.''5 StilL it was not until the middle of the 18th century that the brnin was widely reCQgni;,o,cd as the seat of consciousness. Before then, c:u1didate loc-ations included the hean and the spleen. Paul BrQC:l's ( 1824-1880) study of aphasia (speech dclicit) in br.tin-damagcd patients in I 861 de•non~trJ.tt.-d the existence of localized an:.as of the bmin responsible for specific cog:niti\·e funclions.. In p.1nicular, he showed that speech production was loc:llizcd to the ponion of the left hemisphere now calk"d 81'()(~ ·:~ area. 6 By that time. it W<&S known th:at the brnin consisted of nerve cells. or neum n.s. ~1t it was not unril ISH that Cnmillo Oolgi (1843-1926) developed a staining tedulique all()\lo•ing the Obsel'\•atlon of individual neurons in t~ brain (sec Figure 1.2). This technique w:L." u.~ by Samingo Ramon y Cajal (18...'521934) in his pioneering studies of t~ brain's neuronal structun•s. 7 Nicola.;; Rashevsky (1936. 1938) was the first to :.pply tnathem:nical models to the study of the nervous sytem. t. Si!'K'e tbrn. it 1\ai b«n di~('f(1.! 1hl11 the tree ~hrcw (.blikw.rior) ~a hi&flc'r nzio olbrrun 10 body m:.J.SS.

'

~tany<"ite Ak"anckr

1100<1 ( t8l4) a.c a(IC)Ssibk prior*'~«·

00tJ i persisted in his ~t iff d1a11be br.tin'$ funclions v.'('rt <'~ OUt rrimarily in a ((lnliniiOO$ m<'dium in ..-flidl ~were <"mbcdd«<. .... ~~ CJjaJ rropow'N.I«< •h¢ -~roml doclrinc.'' The: •wo sha~ the Nobel prit.e in 1906 but g~-c n'urually ~lll~Otlislic acccpcai'C'C: spc-«h¢$. '

FiJturt 1.2 The 1).1rt:S or :. nerve ttll ()I' neuron. E..,C"h neui\'Jn C(N't~l$t$ of a cdl body, or $C'Jina, th:n cont:.ins a ttll tmckus. 8 n nchins out (1'()11'1 the cell body ~m: :. number or flber.t C"SIIed dendrites and :t sinsle Ions fiber called tlu! :....:on. 11w: axon stttttht:t aut f()r :1 king dist:t~.ec. cnueh lons cr th:ll'l the: lleale in thi~ di~m indic-:\tes. Typie:.Uy, :m :t~on is I cmloog ( 100 ticne~ the: diamettr of the: ttll body), but e:.n ~:. ch up to I 11.ru:r. A nc:ui'Ofl m:.t~ conn<"ctioos with 10 to IOO.OOOoll.cr neurons :u junctions c:.lled i )'l\:lp;t:t. Signals :m: PI'Ofi'IS:Ited frocn nc:utooto neuron by a CO!tlpliCllttd eleC1toChemical t'C'IlC'tioo. 11'1t sisna1s coo.trol bnlin activit)' in the: shon tC"m\ and :.lso t:ll:'lbk long·tenn changt-s in the rom.ccti\·ity of llC\lfOI\S. ll)C~ mcc-h11nisms arc thoog.tn to fonn the ba.sis for k~ming in li)C br.1.in. M«~t infom1ation proc;e.s.'ling goc.s on in tJ1c: c:crcbn~.l tonex. the outer l;t)'Cr of the llr.lin. ·me Nsit organi:t.miomtl unit appe:ars to be 11column o( ti,swe 11b<M.1t O.S mm in di~nteter. containing 11boot '20.000 ncuron.s :and extending lhc (ull depth of the tortex ••b<M.•t4 mm in humoiJlS).

We n()W' have some data on d.: mapping between artas of the bmin a nd the pans of the body th;tt they control or from which they rcceh·e sen.sory input Soch mappings ~ue abl-e to change radically over Ihe course of a few wccks. nnd some animals seem 10 h.wc muhiple maps. Moreo\'er. we do nOt fully understand h()W' other areas can take O\'er functions when one are-a is damaged. There is almost no theory on how an individual nte•nory is scored. lllc measurement of intncl brnin activity bcg.m in 1929 with the invention by Hans Berger of the e lectroe.nce.l)h,"'logrnph (EEG). The rcce.nt development of fwlCtior"1.1 magnetic R!S001lnc<.' imaging (fMRI) (Ogawa et trl.. 1990; Cnbc.1..1 and Nyberg. 2001) i.o; giving neu· roscientis.IS unprt<:edentedly detailed imnges of brain activity. enabling measurement$ that co~spond in interesting ways to ongoing oogniti\'e processes. 1'bcsc :u\! augmented by advanc<:s in single-cell recording of neuron acth•ity. lndi\•idual ncuror1s c.an be stimulated ell"CCric.ally. chemically. or e\'en optically (H an and Boyden. 2007). allowing neuronal inputoutput relationships to be mapped. Despite these :'ld\•ances. we .,rt still a IOilg way from wulcrstanding how cogniti\'e processes ac.·tuaUy work. llte ttuly amaz.in,g CQnclusioo is that a t'oll~t·tion of simf)/t: ct'lls C"-'l ft•tJd 10 1hought. <JCiiQII, tmd t:OrJScilmSnt3S or. in the pithy wo-rds of John Se:ulc ( 1992). brtthlS (:tm$4' mit~tls.

12

Chapter

Compuuuional units Storo.ge units

I.

Introduction

Supen.:omputer PtllOnal Computer Human Brain 12 1 10 CI)Us. J0 1r.msistors 4 CI'Us. I~ tmru:istors 1011 neurons 101'1 bits RAM 1011 bits RAM 10 11 neurons 101:. bits disk I013 bi1s disk I0 14 S)'n:lps<."S

Cycle time

to-9 sec

w- osec

10- 3 scc

Op~.~rations/sec

tO'~

1010

10 1;

Memory upd;)te.Vsec lOll

LOIO

J0 14

A cn.Mk c:on1p:llison or !he r.•w c:omputo•tion:LI resources ;a~~il;l.blc to the 1BM 6 LUE G ~NE $\lpC-m)II'IP,IICf, •• l ypic:LI person:IJ CQmpvtcr 2008. and the tmnl an brain. 1'hc br.1in's numbel'!l :u~ C$Sentially fixed. wtlen:~!l lhe ~upcrcornpul cr'!lnumbcN ha\'C been in · ~'l't:r>ing by a (;.~~CIOr of 10 c~·cry S yc:us or !00. :t!IQ~ing it 10 :tchicvc I"OUJh parity whh Ihe br.1in. The pcrsor•;al tornputcr l::tgs behitl(l on :.~11 mc1rks exc:cpt cycle t in.c. l"igurt 1.3

or

The only real :dtcmative ti'ICOI)' is mysticism: that minds operate in some mystical realm that is beyond physic.al science. Brnins :md digit.nl computers have somcwhm differcru properties. figure I.3 shows that computers ha'"t a eyde lime that is a million timc$ faster than a brain. TI.e brain ma.kt$ UJ) (or that with fn r more storage and interoonnection than even a high-end personal computer. ahhoug.h 1he larges1 sup<:I\.""'miJUters have a c~pacity thai is similar 10 tl.e brain's. ( It sho\rld be noh."iming to an :q,pro:.-ching: singularity :u which computers re.ach a superhuman level o( pc-.rfonnance (Vingc. 1993: Kunwcil. 2005). but the rnw t:omparisons rue not especially inrommtive. Even "ith a computer or \•inually unlimited cap;Jeily. we still would 1)()1 know how 10 achie,·e the bmin's le,·el of inrelligence.

1.2.5 Psychology • How do humans and :mimals think and act'! The origins of scientific psychology arc usually 1rnccd to the work of the Gc:mum physi· cist llenn:lnn von liehnhohz ( 1821- 1894) and his s:tu
Scc1ion 1.2.

The Nxmdati011s of Anific ial lnteUige.n<.:e

13

th.'lt intfOS)>ection could 1MX provide reliable evidence. Oe haviorisls insisled on studying Oldy objc.•ctivc measures of the pcrttpl~ (or srimulu1) given to an animal and iu resulting ac1ions (or rt'SJI(mse). Beh:wiorism discovered a lot about rots and pigeons but h ad less success ;u undcrsLanding humans.

Cognlth•e psythology. which \•icws: Lhe brJin as: :m inform:uion.process:ing device. can ~ trnn"S of a k nowlcdgc·b.'l.~d agent: ( I ) the s.timulus must be: tranS:Imcd into an inter· rl<"LI ri.1>resent:rtion. (2) the rtprescn t~tion ili manipulated by cognitive pn.lC(.":SSes 10 deri\'C- new intem al rcpresenlntions. :md (3) these are in tum !\!translated back into action. He ciC!lrly explaint.."
After Cmik's ed to address tb::: p$yChology of 11\oCmOry. language. and logical thinking. n::spt."(:tively, II is 1)()w a common (ahhough far from uni"e~tl ) "iew :~mong I)Sychologists that ":t cognitive theory should be like a computer program" (J\nderson. 1980): that is. it sJ)()uld des<:ribe a detailed infonnaliOn· J)I'O<:eSSil\g mechanism \>thereby some cognitive fu nction 1niglu be illlJ)I¢nH"-nled.

1.2.6 Compuler engin~ring • ~low CIUl we build an efficient computer? ~or nrtificial intelligroct' to .suocecd. we mx-d two lhing..~: intelligcnc.-c rulld an Mi(oct. The computer h
14

Chapter

I,

Introduction

Secri.on 1.2.

The Nxmdations or Artilk i:tllntellige.nce

15

World War II . The firs1 Of'rrotional computer was the ek<:trornech:uli<."al tl¢;\th Robinson.3 boil! i:n 1940 by Alan Turing's team for a single purpose: deciphering Gennan mes..Qgts. In 1943. the same grou1> de\·eloped the Colossus. ::. powerrul gcnem.l·purpose mac.hine based on \'3C'UUm tubt.-s.9 The first operational progmmmable computer wns the Z-3. the in\'1!0+

1.2.7

rion of Konr-Jd Zuse in Gennal\y il\ I~ 1. Zuse also inverued tlo:uin.g~J>Oim numbers and the

with a regulalor lhn.l nminln.ined n const:1n1 Row r.ue. This inw.ontion chmtged a}l(! dl'finilion

first high-It-vel progmmming language. Plankalkiil. The llrst eleclmnic computer. the ABC. was assembled by John t\ t:UtasoiT :'tlld his student Clifford Berry berween 1940 :uld 1942 at 10\lo•a State Unh·t~rsi ty. At::u\U.soO'"s research rccd\'t-'d little support or n.ocognition: i.t wa..; the ENIAC. developed as t>art or a secret military projc:e1 at the Univel'$ity or l,cnnsyl,·ania by a team induding John Maudlly and John Ec'ki.'rt. that pro"""(( to be the most inAoential forerunner or modem CQnli)U ICtS. Since that time, e.'ICh generation or com()\lter hardware Ms brought an i nerca..~ in speed and capacity and a detTC.a.se in price. Perrom1;uK"¢ doubled every 18 lncHiths or so until around 2005, when power dissip.
or what :ut wir>'Ct could do. Previously. only livi.ng things could modiry lh<:it behaviOt in response to changes in the environment Other ex.runplcs of self-regulating feedback control systems include the SlC1lm engine gQ\'ernor. cre..'ltcd by James Wan ( 1736--18 19). and th<: UlCnnO$tal. ill\•entcd by Comclis Orebbel ( 1572-1633). who abo in\'enl<"d the submarine. The m :;~;thematk;ll th<.'Ory of stable (e;.'()b;1ck system..; was ck":Vcloped in the 19th ct'tltury. l11e central figure in 1he crcalion or wh:u is now called conln)l th.cory W.'IS Norbcn Wiener ( 1894-1964). \Vttner wru a brilli;mt m:tlhematkian who worked with Bertr.tnd Ru,s. sell. among other.;., berore developing an imercsa in biological and mcchaniet~ l control syS~ems and theircorux"(tion to cognition. Like CrJik (who also used COflti'OI systC111S as psyc:hologi.cal model:s:). Wiener and his colleagues Arturo Ro..">Cnbloeth and Julian Bigelow challenged the beha\'iOri.st orthodoxy (Rosenblueth ~'i!LS of problems that were eneomrxs!>scd in each world view. Calculus ru1d matrix atgebrJ.the tools or control tht."'I)'. k'nd themsel.,.e.~ to systems that :~rc dc:;;cribable by fi xed sets or continuous variable-s. whereas AI wa.~ round.ed in part as a way to e.sca1:1C rrom tlte the:~e peroci\'t.'d limilations. l11e 1ools of logic.al inference and oomputation aliO\Io'ed AI researchers to consider problems such as language. vision, and planning that fe ll completcl)' outside tlte oontrol theorisl's J>urview.

Control theory and cyben1etics

• How <:an an.ir:.cts 01:>erJte unde.r 1heir ()\l.'n control? Ktcs.ibios or Alexandria (c. 2.50 B.C.) buill the firs1 self-<01Urollir1g mach ine: a water c lock.

GOII110.M<:Iffr

4

~

HMOSWIC

~

1.2.8 Linguistics 8 Heath Robinson "'~ a canooni.;1 f11mous for his &pictions of ••hifluk-~ lfld ~surdly ("C)tllptic:o•cd coot~ tions fOf c~·cf)'dJy •~l:s AA'tl as buucrina ~~~. ~ tn the P'OS'"''J' po:riod, TurinJ: v•'»~~cd 10 usc these romp.ncrs for AI ~..~.~t-for cnn11k. Ofl¢ ()(the firs~ c~ progr.~nu (Thring tr ol., 19S3). lliscffon.s •'
• How docs language relate to thought? In 1957. a. f!'. Skinner publishi.'d Verbt1/ Belull'i<Jr. ·n1is was a comprehensive. dctaik-d oc· count of the beOOvioris• appro:1ch 10 language learning. written by lhe forcmoSl expcn in

16

Chapter

I.

Introduction

17

1nathem ~tiC$. b\1t von Neumann rqX'trtcdly said. " If it isn't now. it will be someday." Minsky wa.;; later to prove inllocntial theorcms ~hD"•ing the limitations of ocuml network re.;;cn.rch. l11ert \lo'tre a 11umbtr of early examples of wotk that cru1 be C-har.lcterized as AI. lx•t Alan Turing's vision was perhaps the mosl influential. He gave lecture..; on the topic ILS early

language-it did no1explain how a child eould u.ndcrsumd ruw n1ake up sentences th:u be or

ss 1947 :ull~oe L(X,dOn 1\ll:nhem:uieal Socie1y and :uticul:ued :s petSu:ssive :.genda in his 1950

she h<1d tK•vcr heard before. Chom:<:ky's tht'Ory-ba.q,_'<.l on S)'1llnctic models going back to the

M iele ··Computing Mac-hinery and lntc:lligcocc." ·n,ercin. he introduced the 1'uring Te~. machi•'IC leamin.g. g<:netic :'Ligorithms. and teillfOf'C('mem teaming. He (Xf(lop()S(d the Chiltl Pmgnmm1~ idea. explaining .. lnste:.d of trying to prodl.H."C a prog.r.un me to simulate the adult mind, why noc mther uy to produce one which simulated 1he child 's?"

THE H ISTORY OF ART IFICIAL I NTELLI GENCE

With the lx•ckg:round material behind us. we are rc<~d)' to cover the dt•vclopmc-nt of AI itself.

1.3.1

IOewcl.tltiiJow)

The History of Artili<:iallnteJiige.nce

the fi eld. But curiously. :1 review of the book bt<:ame as well known as the: book iiStlf. and served to ahnost kill off interest in behaviorism. The author of the review wa.o; the linguist N'oom Chomsky. who h:~d j us1 j)Ublished ., book on his own lhOOI)'. Synwctic Srmctun·s. Chomsky pointed out that the behaviorist theory did not addrcs.o; the notion of creativity in

lndi:tn li1lguis1 Pani1li (c. 350 a.C.)-could explain this. and unlike previous d.eories. it was fonnal t'nough that it could in principle be progr.unlll(."<J. Modem linguislics and AI. •hen. v.'tn.: "bom'' ;u :l.botu the s:unc time. ar1d grew Uj) together. intc:I"S<'cling i.n a hybrid licld called computational LinJeuisli<"$ or nuturnl hmguugc processing. n.e problem of undcN.:randing language soon Iun\ed Otll to be considerably more complex than it seemed in 1957. Undcrsl:tnding language requires an under~undi ng of the subjt,"C'' maner Md context. not just an undel'$tanding of the structure of sentences. This might seem obvious, but it wns not widely appreciated until the: 1960s. Much of the c-arly wQrk in knowh.'dg" rtpi'\"Sentation (the study of how 10 put knowk."dge into a fom1 that a c.o mputer can reason with) was tied to language and infonncd by rc~arch in linguistics, which was connec1ed in tum to decades or worl: on the pltilosophi<:al analysis of l:m1;u~ge.

1.3

Sc<:1ion 1.3.

The ges talio n of arlifirial intelligence ( 1943-1955)

The fi rst wort that is now gtnerall)' rccognizcd as AI w-as done by WMten McCulloch and Walter Pius ( 1943). They drew on three soon:es: knowk-dge of the basic physiology and function of I'ICurons in the brain: :t fom1al analysis of proposirionaJiogic due tO Russell Mel \Vhitt·head: and Turing\s theory of computation. Tile)' proposed a model of artificial neurons in which each neuron is charactcri7.ed as being ··on" or "off," with a switch 10 "on" ()C(tltring in n:..,;;ponse to stimulalion by a suflicit·nt number of neighboring I'ICUC\lf~. The st;11e of :. neuron w3s conceived of a:s "factually t."Quivalent to a J>I'Op<)Sition which proposed its adequate stimulus." The)' shO\\·ed. for cx.1mple, that any computable func1ion C0t1ld be computed by some I.Ctwort. of COilJK."<:ted neurons. and thnt aU the logical cOtmeclives (and. or. not. etc.) could be implemented by simple net S-tructures. McCulloch and PitL;; also suggested that suitably dcli1'1Cd I.C:twort.s could learn. Donald Hebb ( 1949) demons1rJted a simple upd~ting rule for modifying the connection strengths between neurons. His n1lc. now calk-d Ucbhian learning. rtmains an infl uential model to this da)'. Two undc:rgradu:LtC students at Uarvard. Marvin Mins.ky und Ocnn Edmon
1.3.2 The birth of artificial intelligence (1956) l>rinceton was home 10 another influential figure in AI. John r-.·tcCMhy. Afic:r receiving his PhD ti'IC-1"¢ in 19SI :md working for two )'ear'S a:s illl instructor. McCarthy moved 10 Strut· ford and then 10 Dartmouth College. which was to become the oNicial birtf.lpl:.cc:= of the field. McCarthy con\•inced Minsky. Claude Shannon. and Nathaniel Roches-ter 10 he-lp him bring together U.S. rese:trohers interc.;;ced in automata theory, neuml nets. and ·the: stud)' of intcl· lig<:rK'C. 10ey o.ganized a two--month wo~shop at D~rtmouth in the sumrner of 1956. The J)I'O~ S1:Hes: 10 We propose that a 2 mOruh. 10 man Study of aniliei:tl intelligence be c:.mied oot during the summer of 1956 lll Dartmouth College in Hanover. New Hamr>shirc. 'l'hc study is to proceed on the ba.sis or the conjecture that e\'CI)' a.;;pcct of learning (1f :Uly Other feature of intelligence C:Ul in principle be so l)f'C(isely de· scrilx-d that a mac-hine can be made to simulate it. An aucmpt will be made to find how 10 make machi1lCS use language. form abSU':'ICiions :~.nd concepcs. solve kinds of problems now rt"$Crved for humans. and impro\•e thcmscl\'eS. We think thalli significant ad\•ance can be made in one or more of these problems if a c.1.tefully sclcctc:d group of scientists work on it together for a summer. There were 10 :mcndces in all, including Trench:1rd More fro m l>rinceton, Anhur Samuel from IBM. and Ray Solomonoff ;uld Olhw Selfridge from MIT. Twtl rese-archers from Carnegie Tech. 11 Allen Newell and Herbert Simon, rather stole the sllOw. Although the othe-rs had ideas and in some cases progr.uns for particular upplications such as chccl.:crs, Newell and Simon already h:td a reasoning program. the Logic 1l!eoris1 ( LT). about w'hich Simon <:l~imed. "We have inve.nted a computer program C;q)able of thinking non·numerically. and thereby sol\'ed the venerable mind-body problem.'' 11 Soon after the wortshop. the progmm wa.<> able to prove most of lhc theorems in Chapter 2 of Rus· 10 1'lli:s '4'4S tk ii,.,:1 ollklal U:Q.ft or MtC'arthy's kt'Ylt "rt{.Miol im~IH~m·~. P
Wein<-r. who was prOfllOiin' ana1of t)~mccic de'\·i~ rnlher 11\atl di!iu.l ton,puters. 11 Now Camrtk Mdkln l/ni,"«Sity (CMU), ., NC~~o'tll alld Simon ll.!so in~n1cd a li51·1'f\l«'.S.••:in; llllfli"Jgl'. I Pl.. 10 '4Ti•e LT. They tud no <"ompilcr W'ld trx~slii•(IJ i• into toot"l\inc: code by hxld. To 3\Xlid nTOI'$, lh¢)' "'Xlfkcd in pm Jiel. C'Jill i~ (lUI biN.ry nunt~rs to each Other :liS they Wrote e;wh insiJ\ICiion tOmJ~ ~ure they 31~·

IS

Chapter

I.

Introduction

Scc1ion 1.3.

covers g•mc playing. and Ch!lplcr 21CXJ>Ioins lhc lcan1ing ICchniques used by Samuel.

The D:uunomh \VOI'irshop did not lead to any IW!W bre:tlnhroughs. bot it did iruroduee

1.3.3 Early enthusiasm, greal expt."Chtlion.s (1952-1969) The early yt:~JS of AI were full of su«essc:s-tn a limite
19

el'$ e01n do only wl~<~.~ they~ told to: his J)r<>_gr.tm quickly learned to play~ bem·-r game than its crrmor. The program was demonsamted on 1clcvision in February 1956. creating a strong impnession. Like Turing. S~m uel h..'ld 1rouble finding computer time. Wotkin_g at ni_ghl. he used machines th:u were still on the testing tloor at IBM·s manufacturing plam. Chapter S

sell and Whitehei!d's Princi'piOttCdly delighted when Simon showed him thai the prog_ram had come up with a proof for one theorem that was shorter than the one in Prindpl(l, The editors oflhe Joumve.menl. and language use. None of the other tielcl~ wen! addressing 1he~ issues. The second answer i.s methodology. A I is the onl)' one of these fl eld5that is ekarly a br.u1<:h of computer scient.-e (although oper.ttions rese~h does sh.'lre an emphasis on compt1ltt simulmions), and A I is the only fiel d to auempt to build machines th.:\t will functi011 autonomously in complex. c.han_ging (.".lwironnlents.

The History of Artili<:ial lntt-Uige.nce

w

John McCarthy moved from Danmouth 10 MIT and there made three crucial contribu· tions in one hiS-toric ye.v: 1958. In M IT AI Lo•1b Memo No. I. MCC.1.nhy defined 1he high·le,·cl J ::mgu ~ge L is p. which w as to become tlK' dominant AI programming langu.:•gc ror lhe ncxt 30 yem. With Lisp. McCanhy h:td 1he tool he needed. but access tOscarce and expensive com· puling fi."$0Urces wa.;: also u serious probil'ln. In response. he nnd others al MIT Lm·cntl"<< lime sharing. AI$C.> in 19S8. McCanhy published a p.-.per em it led Progmms wit lt Commt)lt St'tl$~. in which he described 1hc Advice Taker, a hypo~heli Cttl progrnm that can be seen as 1hc first complete AI s~tcm. Li.ke the Logic 11K'orb:t ;tnd Gt."'mctry Theorem Pro,•er. McC~uth y's program was designed 10 usc knowledge 10 scareh for solutions to problems. But unlike ttl.! Olhe-rs. it was 10 embody ge-neral knowledge of the world, FOr example. he showed how some simple :utioms would enable the progmm to generate a plan 10 drive to the airport. The progr.un was a lso designed to ;K(epl new axiom.;: in the nonn~l course of oOper.n ion. tiK".reby allowing it 10 achieve eompclenc.'C in new nrca.o; witlrom b>~.•itlg reprogrammed. The Advice Taker thus embodied the centr..\1 principles of knowledge represcntatiOft Olnd ~soni ng : th:at it is useful 10 have a fonnal. explicit n:pn."$Ciltation of the world and its workings and to be able to manipulate that rtpresem:uion with deductive pi'OC~~$. It is retMitkable how much of 1he 19S8 paper n::mai1l$ relcv:~m t od~y. 1958 also marl:t..' d the year thm M:m•in Minsky moved to MIT. His initial collabor.uioo wi1h McCanh)' did n01last. hov.-c:\'ct. McC:~tthy S-lressed rtprcscntation a11d teaS<>ning in for· mal logic. wht.'fCa.;; Minsky wn.~ more interested in getting progr.uns to work and eventually developed a.n anti·logic Quilook. In 1963. McCanhy S~:atted 1he AI lab a1 Stanford. His l)lan to use logic to build the ultimate Advice Take r was ad\':UlCI..'d by J. A. Robinson's discovery in 1965 of the resolution mc1hod (a complete lheorem·proving algori1hm for fi rst"()rder logic: Sl"t Ch;1peer 9). Work at Stanford emphasizc.-d gcneml-purpose m.ethods for logical reasoning. Applications of logic included Cordell Green's question-answering and planning systems (Green. 1969b) and the Shakey robotics J)I'Ojc<:t at the Stanford Rescarth Institute (SRI) . The Iauer project discuSSl-d further in Ch:aptcr 25. wa_;: the ti~t 1.t0 demoro;trJie the complete integration of logical reasoning and phys.ieal :K'1ivity. Minsky supc.rvi_s.t._"d ~ series of students who chose limited problems that appean:d 10 require inlelligence 10 soh·c. lbcs.c limited domain.o; became known a." microwQrlds. James Slagle's SAINT prog.mm (1963) was able to soh·e closcd-fonn calculus integmtion problems typical of iirst·)'ear college courses. ·rom Evans's ANALOG\' program (1968) solved srolllCiriC M..'llogy J)tObJems that ~PI:H.!ar in IQ testS. 0 -'lliel Bobrow's STUDENT progmm ( 1967) soh·l"<< algebra story problems. such as the following: I( lhc m,unber or C:U!ii0111CN 10m get,s i,s twice; lhC $CfU.<1le or 20 p::m:nl or lhc number of ad\'c;r1iliCmenls he runs. <1.nd the number of adver1isemt:-nt.s he nm.s is 4:S. wh;&l i.s 1hc number of customers Tom gets?

::.,______________________Chill"" I.

Scc1ion 1.3.

11•e 1-tbtOI)' of Ani6d:allntclligenc·e

21

tbev ability to do lhc-se lhlnp IS IOt."J 10 lti('1U"_"4: r~pldl)' unut-m a visible future-the

..,e ol probkms tbt)• c.v~ hMclk •dl br ('(lf\lm..,,·c ,.,lh tiK range ao "'ilidllhe huma. rniad has b«ft applltd..

T...,. sum os "'isible

""' sa-

r...,.- ...,

b< ,.,.rrprt~td •• ,......., •'*)~. o110 m>dt pmlictJOn.S: dut 'lollhU1 10 )'C'~ a tompukr v.oukl be chess dwnptOR. :and a SJSJ'ific:uu ~ica.l 1hcomn "'"'OUJd ~ pro••ed by mac-hine. 'l"hese pmli<'tions C:Jin( lr\lc (or appro.,im;ucly true) withJR 4() reV'\ nntwr th:.an 10. S1mon's o,·eroontiIOf)' occurred in early machine tr.u~l:ui on t ll'Or1:t, which wert acnerously funded by 1he U.S. Na· 1ioual Researc-h Council in :ut nltc:m1X to liJJC.'td up 1he tnu•dalicm of Russi,.n scientific p:IJX:rs in the wake of the Spulnik launch in 19S7. It w:t" thoug.h t initially 1hnt simple syncac1ic lr.t.ll"• fonuatiOrU based On d.e g,r.amm:m. of Ru"Joi:u' :uMJ Engli~. and "'(It'd replacement from M doctronic dictionary, would suffice 10 ~n·e the cAM~ muninJS of sen.tencc:s.. The fxt is ltw :K"CUn~~re trms.l.aaion requim. bx-l vound lno~ledge in order to resoh-e ambiguity and establish lhe conten1 of thl! srmcn«. The fanlOU' f't'translation of 'lhe sprrit is wdlins but 1he flesh is weak.. as ''lhe ''Odla i)l &ood hu·t the mciiiS ronen.. iUU51n.IC:S the dif6culttt:,. t:on" coun5Cftd. In 1966. a f't"pM by 1n ad"i.;()f)' cornm111tc found ttw ~bas been no madU.ne tnm.biJOO ol gmcn1 sriennfic 1ex1. and new u 1n •rrlln('(bak prospec1.- AU U.S. p"C:mmeru fuodin.& for xademic:" tnn5bltoft proJ«b 'lllb ~led. Today. mxbinc lr.30Sbtion is an im· pc-rf«t bul • -iddy usOO tool for technO. corntnc'f'('Lll. J'O'CI'l'UQCQI. and lmmrt documo'llS. The second tindofdiffi· ormanyoffheprobkm.sttw Al•-u ancmpting to soh-c. Most of the C"MI)' AI procmns ~,·c<J problems by trying out diffm-nt combinations of steps until 1hr ~lut1on "·b found. This str.ucgy worted ini1ially bec:au~ mk·roworkt:s l'Ontained very few obj«h und htlk'e ''et)' few possible aclions and very ~hort solution sequences. Before the lheory or l"Onlj)Ut;~tion:LI complexity was den~loped. it """" widely 1hough1 that ~scaling up" to Iafler problcm:J w~LS simply a matter <>f ra.\ter h;udwnre <'nd larger memories. The &pttmi:s~n th:LI al"'l'OillJ):il1l¢d the dc:\'cl()j)rnent of resolution thoorem proving, for example, was soon d.'lmpcned when f'I:M!ardtcrs (:lil«< lo prow theorem~ involv· in.g m~ lhan:. few dozen (acts. nu·furl tlwt (I JJitiJ;I't.~m t'wr find a sollllitm ;, ,,,.;,,dp/t' dtwJ; I tt# IIINIJI tlwt the pmgrmn NHJUiifiS t#i)' njtlk' mt'<'Jwmi»IIS flc't't/c•J 10 find it itt pmr.lit.'t', 11•e illusion of unlimited computation;tl J:IO\\'C-1" W1~ IM'K rot\fined to problem·soh•ing proy.ams. £arty experi~t~ in m!K'hlne t\-oluiiOrl (now e.Uied genetic algorithnt.ti) (Fried·· befJ, 1958: Friedberg~~"'· · 19S9) welt' ba-.ed on d..e undoubudly correa: belief 1h11 by malang an :appropriat~ series of smJIII mut:JIIOI'\) 10 • machine
FljtUA' 1... A~t'frotnthtbkd:i~'OI'Id. $1tlli)I IJ(Winos,f<'CI.I972) hat.,waromplel«1 I~ commllld '"Find a blod: •hteb it; tAller dun lht OftC' )'01.1 m hold"'J and piolllllfl tbt ben."

lbr meN f.amous m~"'Ofid •-as: the blotts ~'Oftd. v.tbth to•N'-" o( a sa of ~lid bloc:ts plxed on a labktop (or matt oftm. a SlmubtJOn of a ~op). a,:..-"~~ m FiJ.un- 1.4. A l)'pK"ail~l •n 1has •otld is 10 ~ lhe blocb 1n a ll 1960: \Vidrow. 1962). who called his nclwoli:s ndallnes, and by F'rJnl: lloscnbJau ( 1962) with his l>tl'\"tiHronlt. 1lw: ptr«plron OOrl\'('rJttnefl thrort'm (Block n '''" 1962) s.o•tys that the lelurlhtJalgorilhm can ndjus.1 the COit~clion ,olrtn_.gdiS or u ptf'l"t:JUron to m:ltch any inpo1 dnt~.j)ro\•idcd .\UCh #II m:uch existS. TIIese topics are CO\~ red in CNp4c:r 20.

1.3.4 A dose orn:alily (1%6-1973) l~ron• 1he beS-liVllnS· AI researchers w~ noc sh)' abou• mal.u'C pr'l"da(tKJm olthdr comin& ..~~. Tbe folto.in,g su~emen1 by Herbnt Simon in 19S7 •~ ot'k-n quoted.:

h ",_ lh) llil,. to M11pm1e 01' shod:)~ 1ht ~ ,."ll) I CM ~ ~ WA)' thai lht~ ;w QO'III . . lhe worid rnxh~~~n dUI dun&.. QQI k,.. Md lha trt• \IOIW'\n,

"""*

Chapter

22

I.

lncroduction

Se<:tl011

1.3.

F:uluft" to C"'mt to grips ~ ilh tbe "comb~tori.:LI ('\P'o,.on'" "'it.' CliiY of lhe main ait~·

(b) 11 - 28 is a bigb peak: (c) r, - 28 is :1 luJit pcU:: (d ) At k2il ant oi r1 aftd ZJ If. h1P.

c1~ of AI contained in the Li.ghlhill report (l•ahthdl. 1913). v.111C!h fonntd t~ basis foe the d«l'-Kln b) the Brichh JO'o-miiDC"'''ItO end 5Uppor1 for AI rc-r.c:ueh 1n aU bu11wo uni-t'C"~bes.

tbm lbtrt is a tfloat ~

(Onl ndlltOIII p;au'lls :a somt"'ltt b3l difrnml and t:ne~tt colorl'ul pt(N~, v.tlh poiJtK'oal .unbicions

ond prNWI..,imo
lt«
A thatd lhfficulcy arose beausc o( sortX f~l hn·uiJIIOnS on IN blSIC' suuctwes ~~"I ~ to lf1lt'D&e intellipt beha,ior, For cnmple. M m~..y tnd P.lpC'11'~ boot ~~p tmtll ( 1969) pt'O\'ed ltw. allhough perttptrom: (a 3-unpk: fonn of neural necv.oct) oouJd br!roho• n to team an)1hing they were e:~~pable of ~lmJ, thry tould tq)rt~t ''et)' link. In pMIK.'Uin.r, a1wo-input pt<:rceptron (rcstricted to be MmpiC1' th:anthe fonn Rosmbl:m originally 1otuched) 001.1ld not be tQined 10 n:coptiu when its two inpuh w~re dtfft'rtfM. Although their n:"ull" did noc ;~pply 10 mon: complex. mullila)•er l'lCtwork,, ~~.C~ttll funding for neurnl·ntt rt~<'rtll :.c10n d"·indltd to almost noching. Ironically. the new b.'lt'l. ·~'lrOJXI,:ttion ll:':tming :.1· aoritlunt for nmhilnyc:r networks that were to cau~e :m cnom10ut n:'u l'g(n~X: in ncurnl·nc:t l't'ICO'R:h in the late 1980s were 3C'tually di.SCO\'Crtd nNt in 1969 (HI)'M>n .,nd Ito. 1969),

sabk candMblcs monnously. DIL.'I)ItAl. \lob ~trful ~

1.3.5

--

23

The 1-lbtory of AniJiciallntelligcnce

Knowled~e·based

systems: The key lo power? ( 1969-1979)

1'hc pk1u~ of problem solving. Lhat h.:ld :ui~n durin& d)t hN dcc!Mk of AI ~h wa..o; of 11 ~r.al·pu~ ~h mrdunism tryi.n' to stnn& to~;cther dement~ «-a$0nin.a strps to find compt.nc M>lution~ sum appn:lQ(hr.S ha\'t bctn c:alkd \lof:l.k mt thocb ~-although ~r:al. the) do noc SC3k up 10 targr- or dd6cult pobk,m JMI~ The :aJtt"m:.ti'\Y to v.·~;ak mdhod... '"' to ust ~ ~M'UI. dcmain~ific ~led~ thai ~~~~ btJa' CUS()fting ~lind can INft nstl)' hmdk typically ocnmn.a ~ 1n rY-tro'ft ltC'U ol cxperusc. One m1P ..a) that 10 )()1\'e a lwd problem. )"'U haw: ~ almosll.IM.l'fl the ans:v.n alrndy. 1bt 01 'DIAL progr.un (Buchanan~~ o/.. 1969) \lopi.tt a ketone (C.OI '"""""P (•hoc:b ..--.glts 2l!): Jr lht~ :1ft l'a'O pab al z 1 Mel z 2 .Mth lha

ca•r 1

,,

.\1 + ~(JI .s dac nuM>olthe .. hole .nolll'tvltk

All the rdC'\'W tbeofloi.IUI ~ lotdlf 10 I!Oivt" lht",;e pmbl('f1K b• bcea ~pedO\"er (rom geneDJ (Qfm mlbc )~m prtdit'II<MI ron'Jl'Cl'Mnl) ("'fina rnr.ctpleC) to cflk~mt specul forms ("'cookbook r«tpc,"'). (Fcilnlbaum t'l (1./.• 191 1) tl:i

OltlfltM'IM

C81'Nm UC1CIIII

The significance of 0£NORAL wa"' lh:tt it wa~ the fi,..t sue«ssful J:non·lrdge·illl~nsil ~ sys.· 1cm: its expcnise derivc:d from la'le m1mbe~ of St)C(itt.l·pm'JX)S(' n•les.. Later sys1ems ai"' incorpor.ded Lhe main theme of McCianlly\ Adviee T>.ak(•r aPt>roorh-the ck.an separ:nion of the knowledge (in the fonn or nllt.'S) from the rea~oning C(lfnponem, With Ibis leswn in nti1td. Feittl'nb:wm :1nd oil~~ at Stanford began the l-leuriSliC: Pro· gr.tnuning I:Jroject (HI'I)) to inn~..litpte the cxtcm co which the new methodology or UJ~rt systems could be applied 10 other area" or human expcor1i~. n,e next naajOr effor1 was in the 3rea of mrdic.al dia&oosk Fd&cnl»um. 8~h:u1an. and Or. Edward Shortlitrc dc\·cloped MYCIN to di:a.gnose blood infeCtiON. With about 450 ruiC'~. MYCIN w;as able tO perform :\.'\, well a..o; some expem. and com.ldcr.abl y bener lh;~n JUfliot doctors. It also cootainctt '" o major differmcu from DE., ORAL.. F'tN, unhl.t the- DE., ORAl. Nks.. no gener.allbeoftucal modd existed from "hic-h tht M YC" Nk' could be~ "Tbe) h:ld 10 be xquitul from «.tensi\"C irwcnic•rfioJ o( op:ru.. "hom lutn il((flll~ them from kxtboob. odlc:r ~ and dift'ICI ~perimcc o( cases. S«ond. the Nlo hd &o rtftC'C11he UtKlC'fUin1y associaled v. nh mo:tlCOll J.:nooA·kdgc'. M YCIN ~ a C'aln dus of unttruinly called ttrtainly fadors (see Ch3pccf 14), v."hieh seemed (a11he lll'lW) to fil wC'II v.ith ho-•dociM assessed 1he impact of C'\'iden« on the diagnosis. The imponance ()( dom:un l-00\lo ttdcc \1, ~ al,oo tppat('nt in the area. of Uf'lder,q:m :tblc to O\'errome ambiguity and u•'ld<:~ta.nd pl\JfWnm n!rt:reriCcs. bul this wa.;;; mainly be<:aust it w:ts designed spccificall)' for one a~:•-thc blockt world. Seveml researche-rs, including Eugene 0\ami:tl:.. a fell(lw gt:~duat e s.cudcm o( Wino,r.KI'Jo ott MIT. ~ugge:~ted lllal robust l~ngu.-ge 11ndcrst.anding would require &encr.al knowkdit abo111 1he W"Qrkt and 3 general method for u;.ing 1hal knowledge. At Yak. ling.uisi·IUmed·AI·n:~;arthc.-r Roser Schank emphas:i:«d lhi..o; point. daimin&. ""'fhen: is no S«h lhing as ~ym:u:· \lfhtrh up_..cl a lot oflinguiSIJ but did sen-e 10 Sian a u~ful dt..·~."'Us.sion. Schank and his srudcnb bu1l1 a~' o( program.~ (Sch:mk :md Abe-lson. 1977: Wiknsl:y. 1978: Schook ond Ri<sl>«l.. 1961; D)«. 1963) 1hol all hod me toc~ or und«· sundtns Rllur.al bo~. The~~ hooAC\'tt, v.'"ti loess on langu3p: pttr Y and l1lCl«' on tN problems o( rqxt;SC'abnJ and R:ilS(IQ&nJ -. tCh tht ti'IOilo kdJt «qUiml for~ under· The problems oncludod "'~"'""""""' 'l
or

..W""-

..,_ions

Chapter

I.

Introduction

describing human memO')' organizatiOrl (Rieger. 1976: Kolodner. 1983). and ul'lplications to re.aJ.wotld 1>roblc ms caused a concurrent i1'· creao;.: in the demands for work.ablc k.nowiL-dgc representation schcm.:s. A large number

Sc<:1ion 1.3.

The History of ArtiliciallnteJiige.nce

25

Spedt'S ( 1997) suggests th~utbis is the defining dwm<·tuistic of human~t the ffiOSI :lt· ()cnt connectionists questioned whether symbol manipulation had nny re-nl cxplanatOI)' role in detailed models of cognition. i1li.S <1ut$tion remains unanswered. but the eum:nt view is that conneclionist :md symbolic approacht.•s are complemcn£lLI)'. not competing. As occum-d with

of diiTerem represem:nion and re!ISOning languages \\"Cn! de\'eJot,OO. Some were OOsed on

the sep!tr.uion of AI and cogni1ive science. modem n
logic-for cxampk. the Prolog languas.: became popular in Europe. and llll: PLANNER fam· ily in the Uni1e
into two fields. one concerned with creating effective network llll'hitC<'Iun:s and algorithms :~;nd uodctSta.nding their mathem:uical propcnics. the ()ther concen1ed with can:ful modeling of the empirical properties of actual nL'Urons and c nsembks of neurons.

1.3.8 AI adopts Ihe scientific mel hod (1987-pn:senl) 1.3.6 AI bccomt.'S no indus lry (1980-present) The 11~ succl·~o:sfu l commcrti:al expert system. R I. began opcmtion at the Digital Bquipmcnt Corporation (Mc:Dcnnou. 1932). i1lt progr.un helped cor1tigure Ordtl'$ for new computer systems; by 1986. it w:ts saving the comp3ny :m estimated $40 million a year. By 1988. DEC's AI group h;td 40 expen Sy$1ems deployed. with more 011 the way. DuPont h.1d 100 in usc and 500 in development, saving nn cs.timatcd SIO million a year. Nearly CVCf)' major U.S. corporJtiOfl h::td its own AI group tmd was e ither using or iri\'CStig;uing tJ~.pen S)'Stems. In 1931.th.: J:tp:uKose nnnounccd the ..Fifth Generation.. project. a )().year plan to build intelligent compull."-1'$ running Prolog. In response. the United Sunes formed the MicroeltC· tronics and Computer Technology Corporation (MCC) as a research consortium designt.-d to assure national compelitiveness. In both c.,ses. AI w;'LS pan of a broOO erron. including chip design and human·intcrf3Ct' research. In Britain. the Alvey repon reinstated tlll: funding that wa.
1.3.7 The return of neural networks (1986-present) ~

OCH«l11hh$l'

In 1hc mid·l980s at least four different groups reirwentcd the back-propagation lcaming algorithm first found in 1969 by Bryson and Ho. 1bc algorithm was applied to many learning problems in compute r science and psy<:hole>g)'. and the wide-spread dissemination (If the rcsuiL;; in the C'ollection l'amflel Di.ftribmn/ P rocessiJif; (Rumelhan and McClelland. 1986) caused great txc:ittltK".nt. Th.:.se so-calk-d connectionisl models or intcllig.:nt systcm.
Recent )'l"ars have Sl"Cn :t n.ovolution in both the content and the methodology of work i.n :tnificial intclligCOC'e. 14 It is now more common to I;M.1ild on existing thcori<S 1han to propose brJnd·new or)('$. to base claims on rigorou~ theorems or hard experimental t.-viden<:e r:athcr than on intuition. :md to s.l\0\\' relev:ulee to real·workl applications rathe-r tflan IO)' cumples. A I wa" fouNkd in p.'l.rt as a rebellion against the limitations of exisaing fi elds like con1rol theol)' and stati_o:tic-s. but now it is embracing those lields. As David McAIIester ( 1998) put it: In che CO\rl)' period of AI it sccmro plausible ch:u new fom•s of symbolic rompui;Uion. e.g.. ftaiDC$ and .se-mantic ne1wort.s. made much of classic-al theory ob~okcc. lltisiOO to a fom• of isol.:nioni.sm in which AI became largel)' separated from the~~ or computer scic.ncc. Thi.s isolacionism is curn:ntly being abandoned. There is a recognition th:u machine teaming ,should not be i.sol:lll:d from infonn:uion throry. thai unecn:tin reaso11ing shoukl not be isolated from S10Ch:t,stk modeling. th;M SC'li.K'-h $hoold 1)01be: i.solllted from cl;m:i~::~l opcimiz;.1tion ;and ~:omrol. ll1ld tll

Artificial Intelligence: A Modern Approach (3rd Edition)

Read more

Artificial Intelligence: A Modern Approach, 3rd Edition

Read more

Artificial Intelligence: A Modern Approach, 3rd Edition

Read more

Artificial Intelligence: A Modern Approach, 3rd Edition

Read more

Artificial intelligence: a modern approach

Read more

Artificial Intelligence: A Modern Approach

Read more

Artificial intelligence: a modern approach

Read more

Artificial Intelligence: A Modern Approach ( )

Read more

Artificial Intelligence, A Modern Approach

Read more

Artificial Intelligence: Modern Approach

Read more

Artificial intelligence - a modern approach

Read more

Artificial Intelligence: A Modern Approach

Read more

Artificial Intelligence: A Modern Approach

Read more

Artificial intelligence: a modern approach

Read more

Artificial Intelligence: Modern Approach

Read more

Artificial Intelligence: Modern Approach

Read more

Artificial intelligence - a modern approach

Read more

Artificial Intelligence: A Modern Approach ( )

Read more

Artificial Intelligence: Modern Approach

Read more

Artificial Intelligence: Modern Approach

Read more

Artificial Intelligence: A Modern Approach, 3rd Edition (Prentice Hall Series in Artificial Intelligence)

Read more

Artificial Intelligence. A Systems Approach

Read more

Artificial Intelligence: A Theoretical Approach

Read more

Artificial Intelligence: A Systems Approach

Read more

Introductory Econometrics: A Modern Approach, 3rd Edition

Read more

Intermediate Statistics: A Modern Approach, 3rd edition

Read more

Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence

Read more

Multiagent systems: a modern approach to distributed artificial intelligence

Read more

Instructor's Manual for Artificial Intelligence: the modern approach

Read more

Modern physics (3rd Edition)

Read more

Recommend Documents

Artificial Intelligence: A Modern Approach (3rd Edition)

Vice Prc:oi~pb:tn IN rok of learning as wmchng lhe ruch of the ck!itptr 1010 unknown mnronmmb. and •e show hovo' that r...

Artificial Intelligence: A Modern Approach, 3rd Edition

Vice President and Editorial Director, ECS: Marcia 1. Horton Editor-in-Chief: Michael Hirsch Executive Editor: Tracy Du...

Artificial Intelligence: A Modern Approach, 3rd Edition

1 INTRODUCTION In which we try to explain why we consider artificial intelligence to be a subject most worthy ofstudy...

Artificial Intelligence: A Modern Approach, 3rd Edition

1 INTRODUCTION In which we try to explain why we consider artificial intelligence to be a subject most worthy ofstudy...

Artificial intelligence: a modern approach

Artificial Intelligence: A Modern Approach

Artificial Intelligence A Modern Approach SECOND EDITION Stuart Russell Peter Norvig Prentice Hall Series in Artifici...

Artificial intelligence: a modern approach

This page intentionally left blank Artificial Intelligence A Modern Approach Third Edition PRENTICE HALL SERIES IN ...

Artificial Intelligence: A Modern Approach ( )

Artificial Intelligence A Modern Approach Stuart J. Russell and Peter Norvig Contributing writers: John F. Canny, Jite...

Artificial Intelligence, A Modern Approach

...

Artificial Intelligence: Modern Approach

Artificial Intelligence A Modern Approach Stuart J. Russell and Peter Norvig Contributing writers: John F. Canny, Jit...