Table Of ContentThe SoarP apers
Artificial
Integllience
PatriHcekn ryW inston, founeddiintgo r
MichaeBlr adyD,a niel Bobrowa,n dR andaDlalv isc,u rreendti tors
J. G.
Artificiinatle lligiestn hcees tudoyf i ntelliguesnicntegh ei deas maentdh odosf c omputation.
Unfortunateal yd,e finitoifo inn telligseenecmes i mpossibalte t hem omentb ecause
intelligaepnpceea trosb e an amalgaomf s om anyi nformation-procaensdsi innfogr mation
representaatbiiolni ties.
Of courspes ychologpyh,i losoplhiyn,g uistaincds r,e lateddi sciplionffeesrv arious
perspectainvdem se thodologfioerss t udyiinngt elligFeonrc e.t he mosth opwaervter,t, h e
theoriperso posiendt hesfiee ldasr et ooi ncompleatnedt oov aguelsyt atetdob e realiziend
computatitoenramls S.o methimnogr ei sn eedede,v ent hougvha luabildee as, relationships, and
constracianntb se gleanefrdo m traditional ofsw thuadtia ersea ,ft era lli,m presseixvies tence
prooftsh aitn telligiesin ncfa ec tp ossible.
Artificiinatle lligencea noeffwpe errss pectainvdae n ewm ethodoloIgtysc. e ntrgaola li st o
makec omputers intebloltihtg oem natk,e t hemm oreu sefula ndt ou nderstatnhdep rinciples
that maiknet elligpeonscsei blTeha.t i ntellicgoemnptu terwsi llb e extremeulsye ful is
obviousT.h e more profoundp ointi st hata rtificiinatle lligaeinmcset o understand
intelliguesnicneg the ainddme eatsh odosf c omputatitohnu,so fferinag r adicanlelwy a nd
differebnats ifosr theorfoyr matioMno.s to ft hep eopldeo ingw orki na rtificiinatle lligence
belietvhea tt hese thewoirlialep sp ltyo anyi ntelliignefonrtm atiporno cesswohre,t her
biologiocra slo lisdt ate.
Therea res ideeff ecttsh adte seravtet entitoono,A. n yp rogratmh awti lslu ccessfumloldye l
evena smalpla rto fi ntelligweinlcblee inherenmtalsys ivaen dc omplexC.o nsequently,
artifiicnitaell ligceonnctei nuaclolnyfr onttsh el imitosfc omputer-sciteencchen oloTghye.
problemesn counterheadv eb eenh arde nougha ndi nteresetinnogu ght o seducaer tificial
intelligpeenocpeli en twoo rkinogn themw ithe nthusiaIstmi .sn atural, tthhaettnh ,e rhea s
beena steadflyo w ofi deafrso m artificiinatle lligteonc coem putesrc iencaen,d t hefl ow
shownso s igonf a bating.
Thep urposoef t hisse riiensa rtificiinatle lligiestn ocp er ovide peionmp alney areas, both
professioannadl ss tudewnittsht, i meldye,t ailed information iashb aoputp enwihonangt t he
frontieirnsr esearccehn tearlsol v ert hew orld.
MichaeBlr ady
J.
DanieBlo brow
RandalDla vis
The SoarP apers
ReseaorncI hn tegrIantteedl ligence
VolumTew o
Editbeyd
PaulR osenblJooohmnL, a iarndAd,l lNeenw ell
S. E.
THEM IT PRESS
CAMBRIDGMEA,S SACHUSETTS
LONDONE,N GLAND
199M3a ssachuIsnestttisot fTu etceh nology
A© lrli ghrtess erNvoep da.r otft hibso omka yb er eproduicnae ndyf orbmy a nye lectroorn ic
mechanimceaaln( si nclupdhiontgo copying, riencfoorrdmianstgti,oo rnaao ngrde r etrieval)
withopuetr misisniw orni tfrionmgt hpeu blisher.
Printaenddb ounidn t hUen itSetda toefAs m erica.
LibraorfCy o ngreCsast aloging-in-PuDbaltai cation
TheS oapra pe:rr se seaornci hn tegriantteedl liIeg deintcbeeyPd a uSl.R osenblJooomhE,n.
Lairadn,dA llen Newell.
p. cm.
Inclubdiebsl iograrpeehfriecnaaclne dsi ndex.
ISB0N- 262-18(1h5c2)--5I S0B-N2 62-68(0p7b1k-)8
1A.r tifiinctiealll igIe.Rn ocsee.n bloPoaumSl,. IIL.a irJdo,h n1,9 54.I- INIe.w elAll,l en.
Q335.S6139 92
0063.--dc20 91-48463
CIP
Contents
Volume 1O9n6e9:- 1988
AcknwloemdegnIt s
InotdrcutiIo n XIII
XIX
HeuriPsrtiocg rammIilnlg-:S truPcrtoubrleebdmy As .,N ewelIl 3
1 ReasonPirnogb,l Seoml vianngdD, e cisPiroonc esses:
TheP roblSepma caesa F undamenCtaatle gobryAy .N, e welIl 5 5
2
MechaniosfmS sk iAlclq uisiatnidto hnLe a wo fP ractice,
3
byA .N ewelaln dP. SR.o senblIo 8o1m
The KnowledgbeyA .LN eewveellIl, 1 36
4
LearnbiynC gh unkiAn Pgr:o duction-MSoydseotlfeP mr actice,
5
by SP..R osenbalnodAo .mN ewelIl 1 77
A UniverWseaalkM ethobdy, LaiarnddA .N ewelIl 2 45
J. E.
6 The ChunokfGi onagHl i erarchGieense:r aAlM iozdeeodlf P ractice,
7
by P.R oSs.e nbalnodAo .mN ewelIl 2 93
8 TowarCdhsu nkiansag G enerLaela rnMiencgh anism,
by Ladi,rP. SR.o senblaonodAm .N, ew elIl 3 35
f.E .
CONTENVT S
CONTENTS
VI Rl-SoAanrE :x perimienKn nto wledge-InPtreongsriavmemi inan P gr oblem-Solving
ArchitebctyP.u rSRe.o, s enbl]o.o Lmai,r dM, cDertmtoA,.N ewelaln,d OrciuIc 3h4 0
E. ]. E.
9
ChunkiinnSg o aTrh:e A natomoyfa G enerLaela rnMiencgh anism,
by].L airP.d, SR.o senblaonodAm .N, ew elIl 3 51
E.
10
OvergeneraDluirziatnKigno onw leCdogmep ilaitniS oona r,
by]. LairP.d S,.R osenblaonodAm .N, ew elIl 3 87
E.
n
MappiEnxgp lanatioGne-nBearsaeldi oznattSoio oanr ,
byP. SR.o senbalno]do.m LairId 3 99
E.
12
EfficiMeanttc hiAnlgg oritfohrtm hsSe o ar/OPPrSo5d uctSiyosnt ebmyD, .] .S calIe s4 06
13
LearnGiennge rSaela rCcohn trfroolm O utsiGduei dance,
byA .R .G olndgiP., SR.o senblaono]d.m ,L airId 4 59
E.
14
SoaArn:A rchitefocrtG uernee rIanlt elligence,
15
by]. LairAd.,N ewelaln,dP. S.R osenblIo 4o6m3
E.
KnowleLdegveLe ela rniinnS go abry,P. SR.o senblooLmai,r da,n dA .N ewelIl 5 27
f. E.
16 CYPRESS-SAo Caars:Se t udiynS earacnhdL earniinnA gl goriDtehsmi gn,
17
byD .M .S teiIe 5r3 3
VarieotfiL eesa rniinnS go a1r:9 87b,yD .M .S te,i]e.r LairAd.,N ewelP.l ,SR .o senbloom,
18 E.
R.F ylnnA,.G olndgiT., A.P olk,G . iSvheAr.sU ,nr uha,n dG .R .Y ostI 537
0.
DynamAibcs tracPtrioobnlS eoml viinnSg o ar,
byA .U nruhP., S .R osenblaono]d.m ,L aridI 549
E.
19
CONTENTSV II
ElectrMoaniialcn dS cienCtoimfmicu nication:
20
A Studoyft hSeo aErx tendReeds eaGrrcohu pb,y Carley Waenndd tI 563
K. K.
PlaciSnogao rn t hCeo nnectMiaocnh inbeyR, .F lynIn 5 98
21
RecovfreormyI ncorrect KinnoS wolaerbd,yg ]Ee..L airId 6 15
22
Compariosfot nhR ee taen dT reParto ductMiaotnc heforrSs o a(rA S ummary),
23
by NP.ay akA,.G upta, aRnods eP.n bSl.Io o6m2 1
ModeliHnugm anS yllogRiesatsiocn iinSn oga bry, A.P olakn Ad. N ewelIl 6 27
24 T.
BeyonGde neraliazsaS teiaornTco hw:a radU sn ifieFdr amewoforrtk h e
25
AcquisiotfNi eownK nowledbgyP.e S,. R osenblIo 6o3m4
Meta-LeivnSe olasbr ,yP. SR.o sbelnoo]m.E, .L adi,ra nAd. N ewelIl 6 39
26
IntegrMautlitnigSp oluer coefKs n owleidngteDo e signer-Soar:
27
AnA utomaAtligco riDtehsmi gnbeyDr .M, . S teiaenrdA .N ewelIl 6 53
Soar/PSIMn-vEe:s tigatiPnagr aMlaltiecnlahi L sema rnPirnogd uctSiyosnt em,
28
byM .T amb,eD .K alpA,. G uptCa.,L .F orgyB,.G .M ilnaensdA, .N ewelIl 6 59
ApplyiPnrgo blSeoml vianngdL earntionD gi agnosis,
29
by WRa.s hingnta ondP. SR.o senblIo 6o7m4
LearnNienwgT askisnS oabr,yG .R .Y osta ndA .N ewelIl 6 88
30
IndexIA -1( followpiangg7e 0 3)
CONTENTS
VVlolll umTew o1:9 89-1991
A Discusosfi" oThne C hunkionfSg k ialnldK nowledbgyPe a"u Sl.R osenbloom,
31
JohEn.L airandd A llNeenw elbly, B ossIe 7r0 5
T.
A ComparaAtniavley osfCi hsu nkianngdD ecision-ACnoanltyrtoilc,
32
by EtzioniM .aM nidct hel7l1 3
0. T. I
TowaraSd o aTrh eoroyfT akiInngs trucftoiIrom nmse diRaetaes onTiansgk s,
33
byR .L .L ewiAs.N, e wealnld, A.P olIk 7 19
T.
IntegrLaetairnngai nndgP roblSeoml viwnigt haiC nh emicParlo ceDsess igner,
34
byA .K .M odain dA . WetserbIe 7r2g7
W.
SymboAlricch itecfotrCu orgensi tbiyoA n.N, e weP.l lS,.R osenblaono]dm.E, .L airId 7 54
35
Approacthote hsSe t udoyfI ntellibgyeD n.Ac .Ne o,r maIn 7 93
36
TowaradU nifieTdhe oroyfI mmediRaetaes oniinSn oga r,
37
by A.P olAk.,N ewealnld,R .L .L ewiIs 8 13
T.
A SymbolGioca l-OriPeenrtsepde ocntC iovnen ectioannidSs oma r,
38
byP. SR.o senblIo 8o2m1
TheC hunkionfSg k ialnldK nowledbgyP.e S,. R osenbl]o.Eo .Lm a,i radn,Ad . N eweIl 8l4 0
39
A PrelimiAnnaarlyyo sfti hse SAoracrh iteacsat B uarsefoi rsG enerIanlt elligence,
40
byP. S.R osenbloEo.Lm a,i rAd.N, e wealnld, MRc.C arlI 860
J.
TowartdhsKe n owleLdegveie nlS oaTr:he R oloeft hAer chiteicntt uhree UosfKe n owledge,
41
byP. SR.o senblAo.No emw,e alnl]d,.E .L airId 8 97
Tower-NotTirciigngSget rrsa tegy-iCnht ahTneog wee orfH anoAi S:o aMro del,
42
byD .R uiazn dA .N ewell
I 934
CONTNETS IX
43 "BuHto wD idY ouK nowT oD oT hat?":
WhataT heoryA logfo rithm DesignT ePlrUlso ,bc eyDs .Ms . S CtaeniI e9 r4 2
AbstracitnPi roonb lSeoml vianngdL earnibnyAg .,U nruha ndP. SR.o sbelnooIm 9 59
44
A ComputatiMoondaeollf M usical Cr(eEaxttievniAdtbeysd t ract),
45
byS .V icainnaza ndM. J P.r ietIu 9l74a
A ProblSepma cAep proatcoEh x peSryts tSepme cificabtyGi .oR n.Y, os ta ndA .N eweIl 9l8 2
46
1990
LearnCionngt rKonlo wleidnga enU nsupervPilsaendn Dionmga ibny,C .B .C ongnd Io 991
47
Task-SpAerccihfiict ecfotrFu lreexsi Sbylset ems,
48
by R.J ohnsJ.o nS,m itahn,d B .C hanrdasekaIr 1a0n0 4
T W.
CorrecatnidEn xgt endDionmga iKnn owleUdsgien Ogu tsiGduei dance,
49
byJ .E .L airdH,u cMk.Ea .,S .Ya gera,n dC . MTu.c kI 1027
IntegrEaxteicnugt Piloann,n ianngdL, e arniinnS go aforr E xterEnnavli ronments,
50
byJ .E .L airadn dP. SR.o senblIo 1o0m3 6
Soaarsa U nifieTdhe oroyfC ognitSiporni:n g b1y9R 9.L0 .L, e wiSs.B, .H uffmanB,.E .J ohn,
51
J.E .L aird,L ehmaAn.N, e weP.l l,SR .o senbloSoimm,o ann,dS . G .T essleIr 1 044
J. F. T
ApplyiannA gr chitefcotGrue rnee rIanlt ellitgoRe endcuec e SchEefdfuolritn,g
52
by MP.r ietulHasu,D, . M .S te,ia enrdA .N eweIl 1l0 52
J. W.
KnowleLdegveae nld I nductUisveoesf C hunki(nEgB L),
53
byP. S.R osenbalnodoA ma smaIn 1 096
J.
RespondtioIn mgp asisneM se morDyr-ivBeenh aviAo Frr:a mewoforrPk l anning,
54
byP. SR.o senblSo.Lo eme,, Aa.Un ndr uhI 1103
Intelligent AforrcI hnitteegcrtabutyrDie .oMs n. , StIe 1i1e1r4
55
