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7/27/2019 Resources Feature David Wilkins SIPE 2 Script
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DavidEWilkinshomepage:http://www.ai.sri.com/people/wilkins Hispublications:http://www.ai.sri.com/~wilkins/bib.html SIPEhomepage:http://www.ai.sri.com/~sipe/
IamDavidWilkins,aSeniorComputerScientistatSRIInternational,anotforprofit
contractresearchinstituteinSiliconValley.IfinishingmyPhDatStanfordin1979,
myadviserwasJohnMcCarthy,whotaughtmyfirstLISPclass.Iimmediatelywent
acrossElCaminotoworkinSRIsArtificialIntelligenceCenter,primarilybecause
NilsNilssonwasthere.
MyfirstbossatSRIwasEarlSacerdoti,theauthorofNOAH.BeforeNOAH,AIplanninghadbeenasearchinthestatespace.AnexampleofthisisSTRIPS,
developedatSRIforShakeytheRobot.NOAHintroducedplanspacesearchintoAI
planning.
InspiredbyEarlandNOAH,Ibecameinterestedinsolvingrealworldplanning
problemsandfoundthatthisrequiredtheuseofmoredomainknowledgeandmore
expressiverepresentations.
IdividepastresearchinAIplanningintotwocamps:
(1)systemsthattakeaminimalistapproachtodomainknowledge(usingSTRIPS
styledescriptionsofprimitiveactions).primitiveactionplanning
(2)systemsthatfocusonleveragingasmuchdomainknowledgeaspossible.I
focusedonthisapproachindevelopingSIPE(SystemforInteractivePlanningand
Execution).
SLIDE:OilBoomaroundisland
ExampledomainthatwasencodedinSIPE:planningresponsestooilspills.
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Thepictureshowsboombeingplacedaroundanislandtoprotectiffromanoilspill.
Notethattherearemultipleagentsexecutingtheplan,andthatgettingsufficient
boomtosurroundtheislandisimportant.
SLIDE:TechniquesdevelopedinSIPEforRealworldProblemsTheseareExamplesofincreasedexpressivenessandtechniquesformaking
reasoningmoreefficient.Thislistwasdrivenbyclientneedstosolverealproblems.
Someofthesetechniquesarenowcalled"hierarchicaltasknetworkplanning".
MultipleabstractionlevelsManyrealproblemshavedistinctnaturalabstraction
levels,makingthemnaturalfitsforHTNplanning.Also,itisapowerfulwayto
controlsearch,andforhumanstointeractwiththesystem.Itcanbehardto
understandaprimitiveactionplanatthelowestlevelofdetail
ParallelactionsRealisticdomainscanhavedozensof(perhapsnecessarily)
parallelactivities,asactivitiesofvariousagentsarecoordinated.Parallelismcancausecomputationalproblems,andsomesystemsproduceonlysequentialplans.
ContextdependenteffectsRealisticdomainsoftenhavenumerouscontext
dependenteffects.Forexample,whenyoumoveanobject,everythingattachedtoit
orontopofitalsomovestothenewlocation.SIPEwoulddeducethesecontext
dependenteffects,butinSTRIPSonemightneedanoperatorforeverypossible
combinationofobjectsattachedtoorontopofthemovedobject.Thiscancausean
exponentialexplosioninthenumberofSTRIPSoperatorsneeded.
Constraints;Resources
Reasoningwithnumbersisessentialineveryrealisticdomainthatwehavestudied.Commonneedsfornumbersaretime,resourceshavingaspecificcapacityor
availableinlimitedquantities,andgoalsofaccumulation.Anexampleofgoalsof
accumulationisobtainingacertainquantityofresourcethatmustbeassembled
fromsmalleraggregations,suchasgettingenoughboomfromseveralwarehouses
tocontainanoilspill.
Heuristicsandrepresentationstoefficientlyreasonaboutactions
Replanningduringexecutionintherealworld,executionrarelyproceedsas
planned,soSIPEmustbeabletoreplanduringexecution.ManyAIplannersdonot
providesuchacapability.
Interactive,menudriven,graphicalinterfaceSIPEhadperhapsthemostadvanced
GUIinAIplanninginthe80sand90s.Interactingwithpeopleisacriticalaspectof
realworldplanning.Realisticproblemsareembeddedintheworldandgenerallydo
nothavepreciselydefinedboundariesorevaluationfunctions.Itisalsohardto
specifywhenasituationwarrantsbreakingrulesorignoringcertaininformation,
yetsuchsituationsarecommoninreallife.Insuchcases,ahumanusermustbeable
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toguidetheplannerandevaluatetheplansproduced,allowingtheplanningsystem
totakeadvantageoftheusersexpertise.
TheGUIalsoallowstheusertounderstandlargeplansbyviewingthematvarious
abstractionlevels,drillingdownfordetailsasneeded.
SLIDE:screenshotofoilspillresponseplan.
ThisslideshowsascreenshotfromtheSIPEinterfaceofanoilspillresponseplan.
Lookingatthetop3nodes,theblueovalisanactiontodeploy3000feetofboomto
theBerkeleyEelGrassattime3.Theaquacoloredhexagonbeforeitisagoalthat
stillneedstobesolvedatthenexthierarchicallevelofexpansion,togettheboomto
Berkeley.
Finally,notethegoaljustbelowthese2.Infact,thehigherlevelgoalwastoget
9,000feetofboomthere,andSIPEsplitthisinto2actions,getting3,000fromone
location(afactknowntoSIPE)andthenpostinginparallelagoaltogetanother6,000feet.Theentireplanhadafewhundredactionswhencompleted.
Slide:HTNinSIPE
ThefinalslideshowsSIPEinoperation.Ittakes3inputs,ontheleft,representing
theworld(suchastheknowledgeofwherethe3000feetofboomwaslocated),the
operators,whichrepresenttheactionsthatcanbetakenatmultiplelevelsof
abstraction,andthegoals,whicharetobeachievedbytheplan.ThenthePlan
Generatorsearchthoughthisspace,applyingoperatorstoachievegoals.Asdepicted
ontherightside,theexpansiongoeslevelbylevelinthehierarchyuntilan
executableplancomposedofonlyprimitiveactionsisobtained.
TheplanisgiventotheExecutorandifsomethingunexpectedhappens,the
Executorcallstheplanneragainwithanewgoalandnewinformationaboutthe
worldstate,sothatSIPEcanrepairtheplan.
ThisconcludesmybriefoverviewofthemotivationsbehindSIPE.
Formoreinformation,seetheSIPEhomepage,andapagewithmypublications.
Thankyouforyourattention.