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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.