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

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

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

Architecture– PlanFragmentExtractor

Goal: Extractfragmentsfromthecorrespondingsub-reddit• Afragmentisdefinedastherelevantpostthatcontainsinformationaboutachievingagivengoal

Approach:Weutilizesub-reddittagsattachedtopostsmadebyindividualsontheir

timelines

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

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

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:LydiaManikondalmanikon@asu.edu