The Challenge of Anticipation: A Unifying Framework for the Analysis and Design of Artificial Cognitive Systems

294 Pages·2008·3.95 MB·English

by  Giovanni Pezzulo

Lecture Notes in Artificial Intelligence 5225
EditedbyR.Goebel,J.Siekmann,andW.Wahlster
Subseries of Lecture Notes in Computer Science
Giovanni Pezzulo Martin V. Butz
Cristiano Castelfranchi Rino Falcone (Eds.)
The Challenge
of Anticipation
A Unifying Framework for theAnalysis and Design
ofArtificial Cognitive Systems
1 3
SeriesEditors
RandyGoebel,UniversityofAlberta,Edmonton,Canada
JörgSiekmann,UniversityofSaarland,Saarbrücken,Germany
WolfgangWahlster,DFKIandUniversityofSaarland,Saarbrücken,Germany
VolumeEditors
GiovanniPezzulo
CristianoCastelfranchi
RinoFalcone
IstitutodiScienzeeTecnologiedellaCognizione-CNR
ViaSanMartinodellaBattaglia44
00185Rome,Italy
E-mail:{giovanni.pezzulo,cristiano.castelfranchi,rino.falcone}@istc.cnr.it
MartinV.Butz
UniversitätWürzburg
InstitutfürPsychologie,KognitivePsychologieIII
Röntgenring11,97070Würzburg,Germany
E-mail:[email protected]
LibraryofCongressControlNumber:2008934765
CRSubjectClassification(1998):I.2,I.6,F.1.2,G.3,K.8
LNCSSublibrary:SL7–ArtificialIntelligence
ISSN 0302-9743
ISBN-10 3-540-87701-0SpringerBerlinHeidelbergNewYork
ISBN-13 978-3-540-87701-1SpringerBerlinHeidelbergNewYork
Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis
concerned,specificallytherightsoftranslation,reprinting,re-useofillustrations,recitation,broadcasting,
reproductiononmicrofilmsorinanyotherway,andstorageindatabanks.Duplicationofthispublication
orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9,1965,
initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsareliable
toprosecutionundertheGermanCopyrightLaw.
SpringerisapartofSpringerScience+BusinessMedia
springer.com
©Springer-VerlagBerlinHeidelberg2008
PrintedinGermany
Typesetting:Camera-readybyauthor,dataconversionbyMarkusRichter,Heidelberg
Printedonacid-freepaper SPIN:12531196 06/3180 543210
Foreword
The general idea that brains anticipate the future, that they engage in prediction,
andthatonemeansofdoingthisisthroughsomesortofinnermodelthatcanberun
offline,hasalonghistory.SomeversionoftheideawascommontoAristotle,aswell
as to many medievalscholastics, to Leibniz and Hume, and in more recenttimes,
toKennethCraikandPhilipJohnson-Laird.Onereasonthatthisgeneralidearecurs
continuallyisthatthisisthekindofpicturethatintrospectionpaints.Whenweare
engagedintasksitseemsthatweformimagesthatarepredictions,oranticipations,
andthattheseimagesareisomorphictowhattheyrepresent.
But as much as the general idea recurs, opposition to it also recurs. The idea
hasneverbeenwidelyaccepted,oruncontroversialamongpsychologists,cognitive
scientistsandneuroscientists.Themainreasonhasbeenthatsciencecannotbesat-
isfiedwithmetaphorsandintrospection.Inordertogainacceptance,anideaneeds
tobeformulatedclearlyenoughsothatitcanbeusedtoconstructtestablehypothe-
seswhoseresultswillclearlysupportorcastdoubtuponthehypothesis.Next,those
ideasthatareformulableinoneoranothersortofsymbolismornotationarecapable
ofbeingmodeled,andmodelingisahugepartofcognitiveneuroscience.Ifanidea
cannotbeclearlymodeled,thentherearelimitstohowwidelyitcanbetestedand
acceptedbyacognitiveneurosciencecommunity.Andfinally,ideally,theideawill
be articulatedand modeledin such a way thatit is nota completemysteryhow it
couldbeimplementedbythebrain.Thoughtheideathatthebrainmodelsandpre-
dictsandanticipatesissupportedbyintrospectionandalonghistoryofhypotheses,
ithaslargelyfailedontheselatterthreecounts–especiallycomparedwithvarious
theoreticalcompetitors.And this is whythe extentto which ithas beenembraced
bycognitivescienceandneurosciencehasbeenlimited.
But there is good news. Mathematical tools from a number of areas, including
moderncontroltheoryandsignalprocessing,arecapableofallowingforverypre-
cisemathematicalformulationsofthebasicidea,aswellasmanyspecificversions.
This allows for the ideas notonly to be precisely formulated,but also to be mod-
eledandcomparedtohumanbehavioraldata.Andgivena numberofschemesfor
implementingthesekindsofmathematicalmodelsinneuralsystems,itispossible
VI Foreword
toseethesemodelsasbeingimplementedinthebrain.Thequalitativeideathatthe
brainmodelstheworldisfinallybeingclarifiedandquantified.
But we are still in the early stages of this process. While there are many pro-
posalsandtheoriesthatarebeginningtotakeshape,therehavebeenfewsustained
treatmentsofthetopicthatattempttodevelopthemindetailedandconsistentways.
Rather,theapplicationshavelargelybeenpiecemeal.Inthisregardthepresentvol-
umerepresentsa significantadvanceinthe field.Itoffersa sustainedtreatmentof
variousaspects of the generalhypothesis,notonlyin termsof being conceptually
clearandconsistent,butalso intermsofpresentinga wide rangeof particularap-
plicationsthatillustratetheconceptualmachineryinaction.
Itwouldbeanoverstatementtosaythattheideathatthebrainisamodelerand
predictorisrevolutionary,orthatthecurrentswellintheoreticalinterestintheidea
representstheinitialstagesofarevolutionincognitiveneuroscience.Butwhiletalk
ofrevolutionmaybeoverstatement,itcannotbedeniedthatthisapproachtounder-
standingbrainfunctionisbeginningtotakeonanimportancecomparabletothatof
traditionalartificialintelligenceapproachesandconnectionistmodelingapproaches.
The clarity, detail and quality of the ideas presented in this volume, coupled with
thegrowingimportanceofthisgeneralapproach,makethisvolumeacriticalcontri-
butiontoourunderstandingofbrainfunction,andshouldbereadbyanyonewitha
seriousinterestinunderstandinghowthebrainmanagestosupportcognitivefunc-
tions.
July2008 RickGrush
UniversityofCalifornia,SanDiego
Preface
Predictionisdifficult–especiallyforthefuture.NielsBohr
Over the last few decades, it has become increasingly clear that animals most
of the time do not simply react in their world based on unconditioned or condi-
tionedstimuli,butratheractivelyoperateintheirenvironmentinahighlygoal-and
future-orientedway,andnotjustonthebasisofcurrentperception,butinpartau-
tonomouslyfromenvironmentalstimuli.Psychologynowsuggeststhatitisthegoal
itselfthattriggersbehaviorandattention.Learningishighlyinfluencedbycurrent
predictive knowledgeand the consequentdetection of novelty.Behavioral control
ismosteffectivelycontrolledbythehelpofforwardmodelsthatsubstitutedelayed
orthatenhancenoisyperceptualfeedback.Thus,anticipationscomeinmanyforms
andinfluencemanycognitivemechanisms.
Thisbookproposesaunifyingapproachfortheanalysisanddesignofartificial
cognitivesystems:Theanticipatoryapproach.Weproposeafoundationalviewof
theimportanceofdealingwiththefuture,ofgainingsomeautonomyfromcurrent
environmentaldata,ofendogenouslygeneratingsensorimotorandabstractrepresen-
tations.Weproposeameaningfultaxonomyofanticipatorycognitivemechanisms,
distinguishingbetweenthetypesofpredictionsandthedifferentinfluencesofthese
predictionsonactualbehaviorandlearning.Doingso,wesketchoutanew,unify-
ing perspective on cognitivesystems. Mechanisms, that have often been analyzed
inisolationorhavebeenconsideredunrelatedtoeachother,nowfitintoacoherent
wholeandcanbeanalyzedincorrelationtoeachother.Learningandbehaviorare
considered increasingly intertwined and correlated with each other. Attention and
action control suddenly appear as very similar processes. Goal-oriented behavior,
motivationandemotionappearasrelatedandintertwined.
While the revelation of these correlations is helpful for the analysis and com-
parisonofdifferentlearningandbehavioralmechanisms,thesecondbenefitofthe
anticipatoryapproachisthepossibilitytomodularlydesignnovelcognitivesystem
architectures.Thedevelopedtaxonomyclearlycharacterizeswhichaspectsareim-
portant for different anticipatory cognitive modules and how these modules may
interact with each other. Thus, the second benefit of the anticipatory approach is
VIII Preface
thefacilitationofcognitivesystemdesign.Buildingblocksofcognitivesystemsare
proposedandexemplarilyanalyzedindiversesystemarchitectures.Theinteraction
ofthesebuildingblocksthenischaracterizedbytheiranticipatorynature,facilitat-
ing the design of larger, more competentautonomousartificial cognitive systems.
We hope that the proposed anticipatory approach may thus not only serve for the
analysisofcognitivesystemsbutratheralsoasaninspirationandguidelineforthe
progressivelymoreadvancedandcompetentdesignoflarge,butmodular,artificial
cognitivesystems.
Acknowledgments
This work is supportedby the EU project MindRACES, from Reactive to Antic-
ipatory Cognitive Embodied Systems, funded under grant FP6-511931 under the
“CognitiveSystems” initiativefrom the EC. Specialthanksto ourProjectOfficer,
Ce´cileHuet,andtoourProjectReviewers,LolaCan˜ameroandDeepakKumar,for
theirvaluableencouragementandadvice.
July2008 GiovanniPezzulo
MartinV.Butz
CristianoCastelfranchi
RinoFalcone
Contents
PartI Theory
1 Introduction:AnticipationinNaturalandArtificialCognition...... 3
GiovanniPezzulo, Martin V. Butz, Cristiano Castelfranchi, and
RinoFalcone
1.1 Introduction.............................................. 3
1.2 ThePathtoAnticipatoryCognitiveSystems................... 4
1.2.1 SymbolicBehavior,Representation-LessBehavior,and
TheirMergetoAnticipatoryBehavior ................ 5
1.2.2 ThePowerofAnticipation:FromReactivitytoProactivity 6
1.2.3 TheAnticipatoryApproachtoCognitiveSystems....... 6
1.2.4 TheUnitaryNatureofAnticipation................... 12
1.3 AnticipationinLivingOrganisms............................ 12
1.3.1 AnticipatoryNaturalCognition...................... 12
1.3.2 AnticipatoryCodesintheBrain ..................... 15
1.3.3 SimulativeTheoriesofCognition,andTheirUnifying
Nature........................................... 18
1.4 Conclusions.............................................. 22
2 TheAnticipatoryApproach:DefinitionsandTaxonomies .......... 23
GiovanniPezzulo,MartinV.Butz,andCristianoCastelfranchi
2.1 AnticipatorySystems,Anticipation,andAnticipatoryBehavior... 23
2.2 Predictionvs.Anticipation ................................. 25
2.2.1 PredictiveCapabilities ............................. 25
2.2.2 AnticipatoryCapabilities ........................... 31
2.3 AnticipationandGoal-OrientedBehavior ..................... 34
2.3.1 TheAnticipatoryStructureofGoal-OrientedBehavior .. 35
2.3.2 NotAllAnticipatoryBehaviorIsGoal-Oriented........ 36
2.3.3 WhichAnticipationsPermitGoal-OrientedAction?..... 36
2.3.4 The Hierarchical Organization of Anticipatory
Goal-OrientedAction .............................. 37
X Contents
2.3.5 AdditionalElementsofTrueGoal-OrientedBehavior ... 38
2.4 AnticipationandLearning.................................. 39
2.4.1 LearningtoPredict ................................ 39
2.4.2 BootstrappingAutonomousCognitiveDevelopment:
SurpriseandCuriosity ............................. 40
2.4.3 FromWilledtoAutomaticControlofActionandVice
VersaontheBasisofSurprise ....................... 41
2.5 Conclusions.............................................. 43
3 BenefitsofAnticipationsinCognitiveAgents .................... 45
MartinV.ButzandGiovanniPezzulo
3.1 PotentialsforAnticipatorySystems .......................... 45
3.2 PotentialBenefitsofAnticipatoryMechanismsonCognitive
Functions................................................ 48
3.2.1 Effective,Context-BasedActionInitiation............. 48
3.2.2 FasterandSmootherBehaviorExecution.............. 49
3.2.3 ImprovingTop-DownAttention ..................... 50
3.2.4 ImprovingInformationSeeking...................... 51
3.2.5 ImprovingDecisionMaking ........................ 52
3.2.6 ObjectGrounding,Categorization,andOntologies...... 54
3.2.7 SocialAbilities ................................... 55
3.2.8 Learning......................................... 57
3.3 ArisingChallengesDuetoAnticipationsandAvoidingThem .... 60
3.4 Conclusion............................................... 61
PartII Models,Architectures,andApplications
4 AnticipationinAttention ..................................... 65
Christian Balkenius, Alexander Fo¨rster, Birger Johansson, and
VinThorsteinsdottir
4.1 Introduction.............................................. 65
4.2 LearningWhattoLookat .................................. 66
4.2.1 ALearningSaliencyMap........................... 67
4.3 Cue-TargetLearning....................................... 70
4.3.1 CueingbyaSingleStimulus ........................ 70
4.3.2 ContextualCueing................................. 72
4.3.3 FoveaBasedSolution .............................. 72
4.4 AttendingtoMovingTargets................................ 73
4.4.1 ModelsofSmoothPursuit .......................... 75
4.4.2 EngineeringApproaches............................ 76
4.4.3 TheStateBasedApproach.......................... 78
4.4.4 ThePredictionApproach ........................... 79
4.4.5 TheFoveaBasedApproach ......................... 80
4.5 CombiningBottom-UpandTop-DownProcesses............... 81
Contents XI
5 Anticipatory,Goal-DirectedBehavior .......................... 85
MartinV.Butz,OliverHerbort,andGiovanniPezzulo
5.1 ABriefHistoryofSchemas................................. 87
5.2 SchemaApproaches....................................... 88
5.2.1 SymbolicSchemasforPolicyLearning ............... 89
5.2.2 SymbolicSchemasandPredictionforSelection ........ 90
5.2.3 Neural-BasedPlanning............................. 91
5.2.4 NeuralNetwork-BasedDynamicProgramming ........ 92
5.3 InverseModelApproaches ................................. 92
5.3.1 InverseModelsinMotorLearningandControl......... 93
5.3.2 InverseModelsandSchemaApproaches .............. 94
5.4 AdvancedStructures....................................... 94
5.4.1 PredictionandAction .............................. 95
5.4.2 CoupledForward-InverseModels .................... 97
5.4.3 HierarchicalAnticipatorySystems ................... 98
5.5 EvaluationofPredictiveandAnticipatoryCapabilities .......... 99
5.5.1 Schema-BasedSystems ............................101
5.5.2 InverseModelApproaches..........................106
5.6 Discussion ...............................................108
5.6.1 ContrastingPredictiveSystemCapabilities ............108
5.6.2 ContrastingAnticipatorySystemCapabilities ..........110
5.6.3 Integration .......................................112
5.7 Conclusions..............................................113
6 AnticipationandBelievability ................................. 115
CarlosMartinhoandAnaPaiva
6.1 Introduction..............................................115
6.1.1 AnimationandBelievability ........................115
6.1.2 EmotionandExaggeration..........................116
6.1.3 Anticipation......................................117
6.1.4 Anticipation,Emotion,andBelievability ..............117
6.2 RelatedWork.............................................119
6.2.1 OzProject........................................119
6.2.2 EMA............................................119
6.2.3 DuncantheHighlandTerrier ........................120
6.3 Emotivector..............................................121
6.3.1 Architecture......................................121
6.3.2 AnticipationModel................................122
6.3.3 SalienceModel ...................................123
6.3.4 SensationModel ..................................123
6.3.5 SelectionModel...................................123
6.3.6 Uncertainty.......................................124
6.4 Aini,theSyntheticFlower..................................125
6.4.1 EmotivectorsinAction.............................125
6.4.2 Evaluation .......................................128