Knowledge Representation for Agents and Multi-Agent Systems: First International Workshop, KRAMAS 2008, Sydney, Australia, September 17, 2008, Revised Selected Papers PDF
Preview Knowledge Representation for Agents and Multi-Agent Systems: First International Workshop, KRAMAS 2008, Sydney, Australia, September 17, 2008, Revised Selected Papers
Lecture Notes in Artificial Intelligence 5605 EditedbyR.Goebel,J.Siekmann,andW.Wahlster Subseries of Lecture Notes in Computer Science John-Jules Ch. Meyer Jan Broersen (Eds.) Knowledge Representation for Agents and Multi-Agent Systems First International Workshop, KRAMAS 2008 Sydney, Australia, September 17, 2008 Revised Selected Papers 1 3 SeriesEditors RandyGoebel,UniversityofAlberta,Edmonton,Canada JörgSiekmann,UniversityofSaarland,Saarbrücken,Germany WolfgangWahlster,DFKIandUniversityofSaarland,Saarbrücken,Germany VolumeEditors John-JulesCh.Meyer JanBroersen UniversiteitUtrecht,DepartmentofInformationandComputingSciences Padualaan14,DeUithof,3584CHUtrecht,TheNetherlands E-mail:{jj,broersen}@cs.uu.nl LibraryofCongressControlNumber:2009937292 CRSubjectClassification(1998):I.2.4-6,I.2,F.4.1,H.3,H.2.8,F.1 LNCSSublibrary:SL7–ArtificialIntelligence ISSN 0302-9743 ISBN-10 3-642-05300-9SpringerBerlinHeidelbergNewYork ISBN-13 978-3-642-05300-9SpringerBerlinHeidelbergNewYork Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,re-useofillustrations,recitation,broadcasting, reproductiononmicrofilmsorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9,1965, initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsareliable toprosecutionundertheGermanCopyrightLaw. springer.com ©Springer-VerlagBerlinHeidelberg2009 PrintedinGermany Typesetting:Camera-readybyauthor,dataconversionbyScientificPublishingServices,Chennai,India Printedonacid-freepaper SPIN:12780396 06/3180 543210 Preface This book comprises the formal proceedings of KRAMAS 2008, a workshop on Knowledge Representation for Agents and Multi-Agent Systems, held at KR 2008,Sydney, Australia, September 17, 2008. The initiative for KRAMAS 2008 wastakenbylastyear’sKRChairstoenhancecross-fertilizationbetweentheKR (KnowledgeRepresentationandReasoning)andagentcommunities.Topromote participation in the KR schedule, the workshop was conveniently ‘sandwiched’ between days with regular KR sessions. The topics solicited included: – Knowledge representation and reasoning aspects of agent systems (langua- ges, ontologies, techniques) – Reasoning about (actions of) agents – Reasoningmethods(suchasnon-monotonicreasoning,abduction,argumen- tation, diagnosis, planning, decision making under uncertainty, reasoning about preference, ...) applied to agents and multi-agent systems (MAS) – Theory ofnegotiation,communication,cooperation,groupdecisionmaking, game theory for MAS – Cognitive robotics – Representations of other agents / opponent models – Logics for intelligent agents and MAS – Specification and verification techniques for agents – Automated reasoning techniques for agent-based systems – Logicalfoundationsofagent-basedsystems,normativeMASande-institutions – Formal semantics of agent programming languages – Formal techniques for agent-oriented programming and agent-oriented soft- ware engineering We originally received 14 papers. There were two review rounds: the first one deciding on acceptance for presentation at the workshop, and the second one deciding on inclusion of revised, extended and resubmitted versions of the presentedpapersintheseproceedings.Oftheoriginal14,10papersmadeitinto these proceedings. The workshop was a success and proved that there is indeed muchinterestintheproblemsandissuesarisingatthejunctionofKRandMAS. WearegratefultotheparticipantsofKRAMAS2008andtotheauthorswho submitted papers, to the members of the Program Committee for their service in reviewing papers (twice) and to the KR organizationfor taking the initiative tohaveKRAMASandtheirsupportinitsorganization.Thanks,too,toRichard vandeStadtwhoseCyberChairPROsystemwasaverygreathelptous.Finally weareindebtedtoSpringer,andAlfredHofmanninparticular,fortheirsupport in getting these proceedings published. April 2009 John-Jules Ch. Meyer Jan Broersen Workshop Organization Program Chair John-Jules Meyer Workshop Chairs John-Jules Meyer Utrecht University, The Netherlands Jan Broersen Utrecht University, The Netherlands Program Committee Thomas Agotnes Bergen, Norway Natasha Alechina Nottingham, UK Jamal Bentahar Montreal, Canada Rafael Bordini Durham, UK Jan Broersen Utrecht, The Netherlands Mehdi Dastani Utrecht, The Netherlands Giuseppe De Giacomo Rome, Italy Hans van Ditmarsch Otago, New Zealand Jurgen Dix Clausthal, Germany Andreas Herzig Toulouse, France Wiebe van der Hoek Liverpool, UK Wojciech Jamroga Clausthal, Germany Catholijn Jonker Delft, The Netherlands Yves Lesperance Toronto, Canada Alessio Lomuscio London, UK Timothy Norman Aberdeen, UK Henry Prakken Utrecht, The Netherlands Alessandro Ricci Cesena, Italy Renate Schmidt Manchester, UK Carles Sierra Barcelona, Spain Francesca Toni London, UK Rineke Verbrugge Groningen, The Netherlands Table of Contents Reasoning about Other Agents’ Beliefs under Bounded Resources ...... 1 Natasha Alechina, Brian Logan, Hoang Nga Nguyen, and Abdur Rakib Normative Multi-agent Programs and Their Logics................... 16 Mehdi Dastani, Davide Grossi, John-Jules Ch. Meyer, and Nick Tinnemeier Modal Logics for Preferences and Cooperation: Expressivity and Complexity ..................................................... 32 C´edric D´egremont and Lena Kurzen Simulation and Information: Quantifying over Epistemic Events........ 51 Hans van Ditmarsch and Tim French On the Dynamics of Institutional Agreements ....................... 66 Andreas Herzig, Tiago de Lima, and Emiliano Lorini From Trust in Information Sources to Trust in Communication Systems: An Analysis in Modal Logic .............................. 81 Emiliano Lorini and Robert Demolombe Pre-processing Techniques for Anytime Coalition Structure Generation Algorithms...................................................... 99 Tomasz Michalak, Andrew Dowell, Peter McBurney, and Michael Wooldridge Cognitive Use of Artifacts: Exploiting Relevant Information Residing in MAS Environments ............................................ 114 Michele Piunti and Alessandro Ricci Information-BasedArgumentation ................................. 130 Carles Sierra and John Debenham Mediation = Information Revelation + Analogical Reasoning.......... 145 Simeon Simoff, Carles Sierra, and Ramon Lo´pez de Ma`ntaras Author Index.................................................. 161 Reasoning about Other Agents’ Beliefs under Bounded Resources NatashaAlechina,BrianLogan,HoangNgaNguyen,andAbdurRakib(cid:2) SchoolofComputerScience UniversityofNottingham NottinghamNG81BB,UK {nza,bsl,hnn,rza}@cs.nott.ac.uk Abstract. There exists a considerable body of work on epistemic logics for boundedreasonerswheretheboundcanbetime,memory,ortheamountofin- formationthereasonerscanexchange.Inmuchofthisworktheepistemiclogic is used as a meta-logic to reason about beliefs of the bounded reasoners from anexternalperspective.Inthispaper,wepresentaformalmodelofasystemof boundedreasonerswhichreasonabouteachother’sbeliefs,andproposeasound andcompletelogicinwhichsuchreasoningcanbeexpressed.Ourformalisation highlightsaproblemofincorrectbeliefascriptioninresource-boundedreasoning aboutbeliefs,andweproposeapossiblesolutiontothisproblem,namelyadding reasoningstrategiestothelogic. 1 Introduction Thepurposeofthispaperistoinvestigateamulti-agentepistemiclogicwhichresults fromtakingseriouslytheideathatagentshaveboundedtime,memoryandcommuni- cation resources, and are reasoning abouteach other’s beliefs. The main contribution ofthepaperistogeneraliseseveralexistingepistemiclogicsforresource-boundedrea- sonersbyaddinganabilityforreasonerstoreasonabouteachother’sbeliefs.Weshow that a problemof incorrectbelief ascription arises as a result, and proposea possible solutiontothisproblem. To give the reader an idea where the current proposal fits into the existing body of research on epistemic logics for bounded reasoners, we include a brief survey of existingapproaches,concentratingmostlyontheapproacheswhichhaveinfluencedthe workpresentedhere. Instandardepistemiclogic(seee.g.[1,2]forasurvey)anagent’s(implicit)knowl- edgeismodelledasclosedunderlogicalconsequence.Thiscanclearlyposeaproblem whenusinganepistemiclogictomodelresource-boundedreasoners,whosesetofbe- liefsisnotgenerallyclosedwithrespecttotheirreasoningrules.Variousproposalsto modifypossibleworldssemanticsinordertosolvethisproblemoflogicalomniscience (e.g.,introducingimpossibleworldsasin [3,4],ornon-classicalassignmentasin[5]) resultinagent’sbeliefsstillbeinglogicallyclosed,butwithrespecttoaweakerlogic. (cid:2)This work was supported by the UK Engineering and Physical Sciences Research Council [grantnumberEP/E031226]. J.-J.Ch.MeyerandJ.M.Broersen(Eds.):KRAMAS2008,LNAI5605,pp.1–15,2009. (cid:2)c Springer-VerlagBerlinHeidelberg2009 2 N.Alechinaetal. Our work builds on another approachto solving this problem,namely treating be- liefsassyntacticobjectsratherthanpropositions(setsofpossibleworlds).In[6],Fagin andHalpernproposedamodeloflimitedreasoningusingthenotionofawareness:an agentexplicitlybelievesonlytheformulaswhichareinasyntacticallydefinedaware- nessset(aswellasinthesetofitsimplicitbeliefs).Implicitbeliefsarestillclosedunder consequence,butexplicitbeliefsarenot,sinceaconsequenceofexplicitbeliefsisnot guaranteedtobelongtotheawarenessset.However,theawarenessmodeldoesnotgive any insight into the connectionbetween the agent’s awarenessset and the agent’s re- sourcelimitations,whichiswhatwetrytodointhispaper.1 Konolige[7]proposeda differentmodelofnon-omniscientreasoners,thedeductionmodelofbelief.Reasoners wereparameterisedwithsetsofruleswhichcould,forexample,beincomplete.However, thedeductionmodelofbeliefstillmodelsbeliefsofareasonerasclosedwithrespectto reasoner’sdeductionrules;itdoesnottakeintoaccountthetimeittakestoproducethis closure,oranylimitationsontheagent’smemory.Steplogic,introducedin[8],givesa syntacticaccountofbeliefsastheoriesindexedbytimepoints;eachapplicationofin- ferencerulestakesaunitoftime.Nofixedboundonmemorywasconsidered,butthe issueofboundedmemorywastakenintoaccount.Anaccountofepistemiclogiccalled algorithmicknowledge,whichtreatsexplicitknowledgeassomethingwhichhastobe computedbyanagent,wasintroducedin[9],andfurtherdevelopedine.g.[1,10].Inthe algorithmicknowledgeapproach,agentsareassumedtopossessaprocedurewhichthey usetoproduceknowledge.Inlaterwork[10]thisprocedureisassumedtobegivenasa setofrewriteruleswhichareappliedtotheagent’sknowledgetoproduceaclosedset,so, likeKonolige’sapproach,algorithmicknowledgeisconcernedwiththeresultratherthan theprocessofproducingknowledge.In[11,12]Ducproposedlogicsfornon-omniscient epistemicreasonerswhichwillbelieveallconsequencesoftheirbeliefseventually,after someintervaloftime.Itwasshownin[13]thatDuc’ssystemiscompletewithrespect to semanticsin which the set of agent’sbeliefsis always finite. Duc’s system did not modeltheagents’reasoningabouteachothers’beliefs.Otherrelevantapproacheswhere epistemiclogicsweregivenatemporaldimensionandeachreasoningsteptookaunit oftimeare,forexample,[14],whereeachinferencestepismodelledasanactioninthe styleofdynamiclogic,and[15]whichproposesalogicforverificationofresponse-time propertiesofasystemofcommunicatingrule-basedagents(eachrulefiringorcommu- nicationtakesaunitoftime).Inasomewhatdifferentdirection,[16]proposedalogic whereagentsreasonabouteachothersbeliefs,buthavenoexplicittimeormemorylimit; howeverthereisarestrictiononthedepthofbeliefnestings(contextswitchingbythe agents).Epistemiclogicsforbounded-memoryagentswereinvestigatedin,forexample, [17,18,19,20],andtheinterplaybetweenboundedrecallandboundedmemory(ability tostorestrategiesofonlyboundedsize)wasstudiedin[21]. AnepistemiclogicBMCLforcommunicatingagentswithcommunicationlimitson thenumberofexchangedmessages(andconnectionstospacecomplexityofproofsand communicationcomplexity)wasinvestigatedin [20]. InthispaperweexpandBMCL by adding rules for reasoning about other agents’ beliefs, demonstrate that epistemic reasoningdoneinresource-boundedfashionhasaninherentproblemofincorrectbelief ascription,andproposetheuseofreasoningstrategiesasasolutiontothisproblem. 1 Wealsocompletelydispensewiththenotionofimplicitbeliefs. ReasoningaboutOtherAgents’BeliefsunderBoundedResources 3 2 Model ofReasoning Agents ThelogicBMCLpresentedin[20]formalisesreasoningaboutthebeliefsofasystemof reasonerswhoreasonusing propositionalresolutionandcan exchangeinformationto solveaproblemtogether.Thesetupissimilarto,forexample,[22].BMCLmodelseach inferenceruleapplicationastakingasingletimestep,introducesanexplicitboundon thesetofbeliefsofeachreasoner,andaboundonthenumberofmessagesthereasoners can exchange.In this paper,we generalise thisapproachby assumingthat agentscan also reason about each other’s beliefs. Namely, they assume that other agents use a certainsetofinferencerules,andtheyreasonaboutwhatanotheragentmaybelieveat thenextstep.Forexample,ifagentAbelievesthatagentBbelievestwoclausesc and 1 c andthesetwoclausesareresolvabletoaclausec,andagentAassumesthatagentB 2 reasonsusingresolution,thenitisreasonableforagentAtobelievethatagentB may believecatthenextstep. We assume a set of n agents. Each agent i has a set of inference rules, a set of premisesKB ,andaworkingmemory.ToinferfromthepremisesinKB ,therelevant i i formulasmustfirstbereadintoworkingmemory.Weassumethateachagent’sworking memory is bounded by n , which is the maximal number of formulas an agent can M believeatthesametime.Wealsosetalimitonthepossiblesizeofaformula,orrather onthedepthofnestingofbeliefoperators,n ,andalimit,n ,onthemaximalnumber B C ofcommunicationsanagentcanmake.Forsimplicity,weassumethattheseboundsare thesameforallagents,butthiscanbeeasilyrelaxedbyintroducingfunctionsn (i), M n (i)andn (i)whichassignadifferentlimittoeachagenti. B C Thesetofreasoningactionsisasfollows: ReadKB: an agent can retrieve information from its KB and put it into its working memoryusingtheRead action.Sinceanagenthasa fixedsize memory,addinga formulatoitsmemorymayrequireerasingsomebeliefalreadyinmemory(ifthe limitn wouldotherwisebeexceed).Thesameappliestootherreasoningactions M whichaddanewformula,inthataddinganewformulamayinvolveoverwritinga formulacurrentlyinworkingmemory. Resolution: an agent can derive a new clause if it has two resolvable clauses in its memory. Copy: anagentcancommunicatewithanotheragenttorequestaclausefromthemem- oryoftheotheragent.We assumethatcommunicationisalwayssuccessfulifthe otheragenthastherequestedclause.IfagentAhasclausecinmemory,thenacopy byBwillresultinagentB believingthatAbelievesc.Copyisonlyenabledifthe agenthasperformedfewer thann copyactionsin the past andthe prefixof the C resultingbeliefhasnestingofatmostn . B Idle: anagentmayidle(donothing)atanytimestep.Thismeansthatatthenexttime pointofthesystem,theagentdoesnotchangeitsstateofmemory. Erase: anagentmayremoveaformulafromitsworkingmemory.Thisactionisintro- ducedfortechnicalreasonstosimplifytheproofs. Inadditiontotheactionslistedabove,weintroduceactionsthatenableagentstorea- sonaboutotheragents’beliefs,essentiallyepistemicaxiomsK(ascribingpropositional 4 N.Alechinaetal. reasoningtotheotheragent)and4(positiveintrospectionabouttheagent’sownbeliefs, andascribingpositiveintrospectiontootheragents).Thereasonswe donotadoptfor exampleKD45are as follows.Ifthe agent’sknowledgebase isinconsistent,we want ittobeabletoderiveB⊥(orB[]where[]istheemptyclause).Negativeintrospection is also problematic in a resource-boundedsetting, in that the agent may derive ¬Bα ifαisnotinitscurrentsetofbeliefs,andthenderiveαfromitsotherbeliefs,ending upwithaninconsistentsetofbeliefs(¬BαandBαbypositiveintrospectionfromα), evenifitsknowledgebaseisconsistent.Wecouldhaveadoptedarestrictedversionof negativeintrospection(see,e.g.,[12])butinthispaperweomititforsimplicity. In addition to the reasoning actions listed above, we therefore add the following actions: Other’sResolution: anagentAcanperformthisactionifitbelievesthatanotheragent B believes two resolvable clauses c and c . Then A can conclude that B will 1 2 believein the resolventclause c of c andc in the nexttime point.As a general 1 2 case, we can extend the chain agent-believes ... agent-believes. For example, if agentAbelievesthatagentBbelievesthatagentCbelievestworesolvableclauses c andc ,thenitispossibleinthenexttimepointthatagentAbelievesthatagent 1 2 BbelievesthatagentC believescwhichistheresolventofc andc . 1 2 PositiveIntrospection: ifanagentAbelievesaclausec,itcanperformthisactionto reachastatewhereitbelievesthatitbelievesc. Other’sPositiveIntrospection: ifanagentAbelievesthatanotheragentBbelievesa clausec,itcanperformthisactiontoreachastatewhereitbelievesthatBbelieves thatBbelievesc. The reasoningactionsPositive IntrospectionandOther’sPositive Introspectionare onlyenabledifthederivedformulahasadepthofnestingofatmostn . B Note that the assumption that the agents reason using resolution and positive in- trospectionis not essential for the main argumentof this paper. This particular set of inferenceruleshasbeenchosentomakethelogicconcrete;wecouldhave,forexam- ple,assumedthattheagentsreasonusingmodusponensandconjunctionintroduction insteadofresolution.Inwhatfollows,wegiveaformaldefinitionofanepistemiclogic forcommunicatingagentswhichreasoninastep-wise,memory-boundedfashionusing somewell-definedsetofinferencerules. 3 Syntax andSemantics ofERBL Inthissection,wegivethesyntaxandsemanticsofthelogicERBLwhichformalises theideassketchedintheprevioussection.ERBL(EpistemicResourceBoundedLogic) is an epistemic and temporal meta-language in which we can talk about beliefs ex- pressedintheagents’internallanguage. Letthe setofagentsbeA = {1,2,...,n }.We assumethatallagentsagreeona A finitesetPROP ofpropositionalvariables,andthatallbeliefformulasoftheinternal language of the agents are in the form of clauses or clauses preceded by a prefix of beliefoperatorsoffixedlength.
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