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Extracting Incomplete Planning Action Models from Unstructured Social Media Data to Support Decision Making
Lydia Manikonda↑, Shirin Sohrabi‡, Kartik Talamadupula‡, Biplav Srivastava‡, Subbarao Kambhampati↑↑Arizona State University‡ IBM Research
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Motivation
Planning and decision-making techniques depend heavily on the availability of complete and correct models.
01Most existing planning and decision-making methods assume hand-coded, fully-specified action models a priori.
02Dependence on hand-coded, fully-specified models restricts the number and types of domains where planning can be used.
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“Planning”ismorethan justplansynthesisfromhand-codedmodels
Figure1:ASpectrumofIncompleteDomainModelsandassociatedplanningcapabilities
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Motivation• Awealthofuser-generateddataisavailableonline– especiallyononlinesocialmediaplatforms
• Alotofthisdataiscrowd-generatedwisdomaboutactionsandplanstotakeinordertoachievecertainhigh-levelobjectives
• E.g.Health&Fitness,EventPlanning,TravelPlanning,AcademicPreparation
• Growinginterestinexploitingthisdatatoprovidedata-baseddecisionsupport,especiallybyextractingcausalrelationships
• DecisionSupport:Canbeplansynthesis,butcanalsobe…• PlanCritiquing• PlanRanking• PlanRetrieval• PlanVisualization,etc.
Planning in the Data-Centric AgePanel on Wednesday
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ProblemStatement
1. Canweextractapproximatelystructuredplantracesfromunstructuredsocialmediaposts?
2. Usingtheseapproximateplantraces,canwebuildanapproximatecausalmodel?
Causalmodelscanthenbeutilizedtoautomaticallyexplain(planexplanationorplancritiquing),visualize,retrieve,orranktheexperiencessharedbyusersononlinesocialmediaplatforms.
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Challenges
• Extracting actionsfromunstructuredusers’posts•Processing theextractedactionstoreduceredundancy
•Building plantracesfromtheextractedactions•Constructing anactionprecedencegraphfromthesetraces
•Planning usingtheseprecedencegraphs7
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SocialMediaData:Reddit
QuitSmoking Couchto5K WeddingPlanning
#Users 787 604 969
Total#oftraces 1598 1131 3442
Average TraceLength 17.97 16.7 21.29
#Uniqueactions(orig) 1712 1299 2666
#Uniqueactions(model) 234 194 355
#Pre-actions 1499 1060 2795
#Post-actions 1398 982 2619
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SolutionArchitecture
InputSocialMediaData
PlanFragmentExtractor
ActionExtractor Generalizer Trace
Builder
SequenceProbabilityLearner
ModelValidator
OutputActionModels
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Architecture– PlanFragmentExtractor
Goal: Extractfragmentsfromthecorrespondingsub-reddit• Afragmentisdefinedastherelevantpostthatcontainsinformationaboutachievingagivengoal
Approach:Weutilizesub-reddittagsattachedtopostsmadebyindividualsontheir
timelines
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Architecture– ActionExtractor
•Goal: Extractactionnamesalongwiththeirpredicates
•Approach: Utilizethepartofspeechtaggertoidentifytheverbs(actions)andnouns(predicates)
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Architecture– Generalizer
• Goal: Duetohandlingunrestrictednaturallanguagetext,weneedtogeneralizeasetofactionstotheirparentaction,toremoveredundancy
• Approach: Hierarchicalclustering approachwithLeacockChodorow (LCH)similaritymetrictocomputethedistancebetweenanytwogivenactions
• LCH:Usedincaseswheretherearedistancesbetweenobjects,andataxonomythatrelatesthem
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Architecture– TraceBuilder
•Goal: Representtheactionsintheplantraceswiththeircorrespondingclusterrepresentatives
•Approach:Mapthecorrespondingclusterrepresentativestotheactionsandrepresenttheplantracesintermsoftheserepresentatives
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Architecture– SequenceProbabilityLearner
• Goal: Extractthepre-actionsandpost-actionsforanygivenactionbyusingprobabilitymetrics
• Approach: Data-drivenconditionalprobabilityestimationbetweenanytwogivenactions(ai andaj)
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Architecture– ModelValidator
• Goal: Tomeasuretheexplainability ofthecausalmodelderivedfromtheplantraces
• Approach: Buildthemodelusing80%ofthedata(T)andtestitusingtheremainingdata(T’)byconsideringametriccalledexplainability:
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SampleRelationshipsExtractedQuitSmoking Couchto5K Wedding Planning(:action change(ability)[:pre-action eat(gross) crave(succeed) dealt(reality)][:post-action set(goal) run(mile) quit(smoke)] )
Candidate Explanation:Someoneiscravingsuccessandisdealingwiththerealityofeatinggrossfood.Thispersonwantstochangetheirabilitiesandsosetssomegoals,runsmiles,andquitssmoking.
(:action sign(race)[:pre-action recommended(c25k) push(run) refer(program)][:post-action begin(week) run(minute) cover(mile) know(battle) kept(pace)] )
CandidateExplanation:Apersonwasrecommendedthecouchto5K andwasencouragedtorun.Hereferstoaprogramandsosignsupfortherace.Hethenbeginstorunafewminutesfromthenextweekandcoversafewmiles.Heknowsthatitisabattlebuthaskeptthepace.
(:action hate(dress)[:pre-action pick(dress) saw(dress) blame(problem) cost(much)] [:post-action kill(wed) find(dress) move(wedding)] )
Candidate Explanation:The personpicksupthedressandseesthedress.Itmaycostalot,andthepersonstartslookingatthisasareasontokillthescheduledwedding.Eventuallythepersonmustfindanewdressandmovetheweddingdate.
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Evaluation1:Explainability(10-foldcross-validation)• Onewouldexpectthatastheclusteringthresholddecreases,theexplainabilitywouldincrease
• Reason: Redundancyofactionsdecrease
α QuitSmoking Couchto5K WeddingPlanning2.50 65.66% 64.5% 73.39%
2.25 65.66% 64.59% 73.39%
2.0 68.41% 69.78% 77.7%
1.75 69.33% 70.67% 78.39%
1.50 80.58% 82.03% 84.68%
1.25 90.42% 89.43% 91.6%
1.0 89.31% 89.91% 91.04% 17
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Evaluation2:Soundness&Completeness
• Soundness:Whetheragivenshallowworkflowismeaningfulandcanhelpachieveagoal
• Completeness: Ifagivenshallowworkflowismissinganyimportantactionstoachieveagivengoal
• HumanSubjectEvaluation:N=10
Domain Soundness Completeness
QuitSmoking 42% 38%
Couchto5K 66% 45%
WeddingPlanning 36% 43%
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ContributionofourApproach
Existingapproaches:• Assumethatthemodelisalreadygivenup-front
• Utilizealmostcompletelystructuredtracestobuildthemodel
• Considerasingleplan-traceforagivendomain
Proposedapproach:• Aggregatesexperiencesofmultipleusersforagivendomain
• Utilizesrawunstructurednaturallanguagedata
• Extractsstructuredplantracesbyestablishingsequentialitybetweenactions
• Buildsanapproximatecausalmodel• Robustmodelthatgeneratesmeaningfulandusefulplans
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CapturingPreferences:AnExtension
• Input• Setofplantraces,innaturallanguage,ofusersperformingsomeactivities• Socialnetworkofpeoplesimilaranddis-similartoagivenperson
• E.g.:Twittertimelineofapersonwhowantstoloseweight
• Output• Domainmodelpersonalizedforapersonconsistingofstatevariables(predicates)andactions
• Notes• Learnedmodelpersonalizedforanindividual,andprobabilisticallyweightedbythechoicesofothersintheirsocialgroups(e.g.“near”and“far”)
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PriorWork
PriorArt/Criterias Preferences SocialNetwork Learning
NoStructured
PlanTraces
HTN AIPlanning
HTNPlanning(e.g.,Nauetal.2003) ✗ ✗ ✗ N/A ✔ ✔
HTNPlanningwithPreferences(e.g.,Sohrabietal.2009) ✔ ✗ ✗ N/A ✔ ✔
LearningHTNorNon-HTNPlanning(e.g.,Yangetal.2007) ✗ ✗ ✔ ✗ ✔ ✔
LearningHTNPlanning+Preferences(Lietal.,2012) ✔ ✗ ✔ ✗ ✔ ✔
LearningNon-HTNPlanning+Preferences(e.g.,Bryceetal.2016)
✔ ✗ ✔ ✗ ✗ ✔
AIPlanningwithPreferences(e.g.,Baieretal.,2009) ✔ ✗ ✗ N/A ✗ ✔
PreferenceElicitation/Learning(e.g.,Boutilieretal.2004) ✔ ✗ ✔ ✔ ✗ ✗
LearningfromSocialNetworks(e.g.,Rajaram,etal.2013) ✔ ✔ ✔ ✔ ✗ ✗
ProposedExtensionofThisWork ✔ ✔ ✔ ✔ ✔ ✔
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ConclusionsandFutureWork• Extractingmodels– directlyfromunstructureddata– tosupportsequentialdecisionmakingischallenging
• Byexploringthechallengesandtomeasurethefeasibilityofbuildingusableactionmodels,amulti-phasepipelineisproposed
• Thepipelinetakesunstructureddataasinputtoautomaticallygenerateanapproximateorincompleteactionmodel
• Evaluationsbyhumanraterssuggestedtheplansaresoundtoacertainextent
• ExaminingusewithcurrentPDDL-styleplannersfuturework
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FurtherQuestions&Comments:[email protected]