Integration and Action in Perception/Action Systems with Access to NonLocal-Space Information
From: AAAI Technical Report WS-96-07. Compilation copyright © 1996, AAAI (www.aaai.org). All rights reserved.
Glenn S. Wassonand WorthyN. Martin
ComputerScience Department, Thornton Hall, University of Virginia, Charlottesville,
{ wassonI martin }@virginia.odu
Abstract
Actionsand objects are closely tied together(Russell&Norvig
1995).Weoften think of the actions wecan performon objects as
propertiesof those objects. Thetheoryof action presentedhere,
containsthe belief that actionsare definedin termsof the objects
they effect. Intuitively then, actions consistof a verbanda noun,
for example,pick-up-the-soda-can,
go-to-the-car,or paint-the-canvas. Weare interestedin twodifferentroles for nouns,as direct objects whichthe agentmanipulates(the-canin pick-up-the-can)and
as indirect objectswhichspecifydestinations(the-cat in go-to-thecar). Werefer to these nounsas m-roleobjectsor d-role objects.In
our theoryof action, before performinganyaction, an agent first
developsa deietic representationof the m-roleor d-role object.So,
the agentidentifies the-soda-can,the-car, or the-canvas.When
this
is completed,
the specifiedactioncan be takenusingthe objectindicatedbythe representation.Aplanis, therefore,a set of objects
and the actions to perform.
Wecontendthat this deictic organizationis an effective means
of expressingactions becausean action cannottake place without
identifyingthe mor d-role objects. Ourresearchwill showthat actions can be performedeffectively and plans can be organizedappropriatelyusing such a representation.Wewill presenta system
that usesdeictie representations
as a meansto integratea deliberate
plannerand a perception/actionsystem.Non-local-space
information in the planswill be conveyed
to the perception/actionsystem,
whereit can be actedon.
Emergent
fromour theoryof activity is a representationsystem
that integratesagent controlsystemcomponents.
Ourtheoryis centered onthe real worldobjects effeetedby action. Webelievethis
providessufficient and effectiveorganizingprinciplesfor designing perception/actionsystems.
Background
There are two schools of thought in the AI planning community, the classical planning camp[see (Tate, Hendler
Drummond1990) for a survey] and the reactive camp
(Brooks 1986)(Firby 1987). The classical planning proponents claim that complextasks cannot be completedwithout
reasoning about the state of the world in advance, while the
reactive proponents claim the world is too dynamicto reason about or evenrepresent. Clearly, a little of each is true.
One source of information, commonto manyclassical
planners, but inaccessible to pure perception/action (i.e. reactive) systems, is non-local-space information, such as is
present in a map.A perception/action systemoperating in a
large environment maynot be able to perceive its entire
route space a priori. Somedeliberative entity must plan a
137
VA22901
course for the agent and activate appropriate behaviorsat the
correct time. This is because a perception/action (PA) system has no memoryor representation. It cannot create a
plan, it only knowswhat action should be taken now. Although PAsystems can perform somenavigation tasks without consulting a deliberator, whenthe agent reaches a Tjunction for example, the perception/action system must appeal to its route plan in order to deterministically choosethe
correct direction. This paper’s agendais not to discuss better
methodsof plan generation, but to discuss howto better
communicatethe information expressed in plans to a perception/action system. That is, howto integrate a non-localspace system component and a PA system component by
transferring non-local-space information to the PAcomponent. in such a waythat the PAcomponentcan use the informationefficiently and effectively.
Integration
Traditionally, perception/action systems are thought of as
determining their actions directly from their perceptions.
This can be expressed as a =tiP), wherea is the current action and P is the set of current precepts. Workby Brill (Brill
1995) has shownthat perception/action systems can exhibit
greatly improved performance by incorporating small
amountsof task-dependent representation into their framework. In such systems, a = f(P, M). wherea and P are as before and M is the PA component’s
memory or
representation. Brill proposes a deictic, local-space representation system based on Ullman’s (Ullman 1984) markers,
as used in (Agre &Chapman1987).
Since perception/action systems can use such local-space
representations effectively to achieve certain complexbehaviors, then the task of the entity possessingthe non-localspace representation is to transfer this infoLmationinto the
PAsystem’s local-space representation. Given the formulation, a = f(P, M), the mostefficient wayof influencing a PA
system’s actions is through M.
However,there is a temptation to pass arbitrarily complex
data structures to the PAsystem through M. In order for a
PAsystem to remain effective in dynamicenvironments, it
cannot be asked to manipulate large data structures. We
present a minimalist interface between a non-local-space
componentand a perception/action system, using markers.
Markers
Reactivesystems, as conceivedby Brooks(Brooks1986),
do not attemptto modelthe worldbecauseanystored representationwill become
stale. In fact, the designphilosophy
of
suchsystemsis that the worldis a better modelof itself than
an agentcouldever build. Therefore,an agentneedsno representationbecauseit can just "look"at the worldto determinewhateverit needs to know.
Clearly, there are somesimpletasks whichrequire some
formof representation.For example,whenI sit in an easychair, I align myselfwith the chair, but whenI actually sit
down,I amfacing awayfrom the seat. However,becauseI
havea representationof wherethe seat is, whichI developed
whilelookingat it. I amable to maneuver
myselfinto the
chair. If a purereactivesystemweretrying to sit down,once
it is nolongerfacingthe chair, thereis nothingin its perceptive field to indicatethat it shouldsit down.Withoutrepresentationto tell the systemthat it wastrying to sit, it never
will.
Amoresophisticated systemwhichcould sit in the chair
at mydesk mightuse the computermonitoras a visual cue
to decidewhento sit, i.e. whenyousee the monitor,the chair
is behind you, so sit down.However,mydesk chair has
wheelsand can movearound.In order to decidewhatvisual
cuetells the agentwhen
to sit for variousinstancesof the sitting task, the sit routinemustbe parameterized.
Anothersystem componentmust pass this parameter to the reactive
system.Thisinteractionis discussedin sectionrifled "Transferring Non-Local-Space
Informationto a PASystem".
Tryingto preserve the robustness of PAsystems, while
incorporating representation involves using a minimalist
representationfor anygiven task. Taskdependentrepresentations give us a minimalist
deicric representationin that the
agent modelsonly those objects whoseroles are relevant to
its currenttask. Thereasonthat onlytask dependent
objects
are modeledis that the high cost of keepingan elaborate
modelup-to-date limits PAsystemeffectiveness.
Markersare the basis of the representationin our theory
of action. Ullman(Ullman1984)uses the term "marking"
refer to remembering
a location for later reference. Hesupposes the creation of a "markingmap"whichholds coutext
dependentlocation, in the visual field, that havebeenanalyzed. Attneaveand Farrar (Attneave& Farrar 1977)suggest that locations outsidethe visual field can be marked.
Pylyshyndescribes a similar concept in his FqNST
model
(Pylyshyn&Storm1988). Hedescribeda limited number
"reference tokens"whichcan be boundto visual features.
This binding is a pre-requisite to determiningrelational
properties about those features. Agreand Chapman
(Agre
Chapman
1987)use markersin their Pengisystemto identify objectsrelevantto the penguin’scurrenttask, e.g. the-iceblock-blocking-my-escape.
Brill considers issues involved
in using markersin dynamic3Ddomainsinvolving occlu-
138
sion (Brill, Wasson
&Martin1996).
Markersare a task dependentrepresentation in which
each markeris associated with an object havingsomerole
(mor d) in the task. Markers
are generallythoughtto contain
a ’what’ and a ’where’ component.The ’what’ component
describesthe object’srole in the currenttask. Themarkerassociatedwith a particular object mayhavedifferent ’what’
components
for different tasks. For example,an electric
socketcouldbe markedas an obstaclefor navigation,unless
the agentneededto recharge,in whichcase, the samesocket
could be markedas goal. The ’where’ componentcontains
the locationof the associatedobject in somecoordinatesystem useful to the current task. Themarkersin our system
have other componentswhichare discussed in the implementation section. However,the total numberof components in a markeris purposelykept smallin order to avoid
the data manipulationproblemsdiscussedearlier.
Expectants
Sincea markeris associatedwith an object, an important
question is howthe association is made.Pylyshynsays the
associationis madethrougha "primitiveoperatiouwhichis
pre-attentively performedon the visual display" (Pylyshyn
& Storm1988). This suggeststhat there is a processexamining the visual field for propertieswhichcanbe detectedin
a pre-attenrivefashion. However,
it is unlikelythat all the
pre-attenfive properties whichhumanscan detect are being
soughtat all times. Rather,weonly detect the featuresthat
are importantto our current task. Giventhis task dependence,it is likely that the currenttask candictate not only
whichfeatures oneexpectsto fred, but whereonemightexpect to f’mdthem.
In orderto facilitate non-local/localspaceintegration.We
define the term "expectant".Anexpectantdefmesa set of
objects whichmatchsomedescription of an object. The
morespecificthe description,the fewerobjectswill be in the
expectantset. For example,supposemytask wasto tighten
a screw.I knowthat myscrewdriversare in the basementon
the workbench.Theremaybe multiple screwdriverson the
workbenchand each of these can matchthe expectant description. Theexpectantdescribesthe objectI need,a screwdriver, andwhereI mayfred that object, on the workbench.
Thedifference betweena markerand an expectantis that a
markeris associatedwith a specific object, whilean expectant describesa set of objects.
Transferring Non-Local-Space Information to a
PA System
Communication
betweenour system componentpossessing non-local-space(NLS)informationand our perception/
action component
occursvia nninstantiatedmarkers."Uninstantiated"reflects the fact that the markeris associatedwith
an expectantandhencea single object has not yet beenassociated with the marker. TheNLScomponent
can pass unimtantiated markersto a PAsystemfor instantiation. The
PAsystemmustdetect andselect a single object fromthe set
describedby the expectantandassociateit with the marker.
Making
this associationmeansthe markeris nowinstantiated. So, it is nowwithinthe local-spacerepresentationof the
PAsystem,whereit can be used effectively.
Consideran agent whosetask is to get a cup of coffee.
Oneof the steps in this planis to get a coffeemug.Thecoffee mugsare in the cabinet abovethe coffee maker,on the
bottomshelf. When
the cabinetdooris closed,the agentcannot perceive any mugswhichmaybe in the cabinet. The
NLScomponentpasses an uninstantiated ’mug’markerto
the PAsystem.The’where’slot of this markertells the PA
systemthat the mugwill be inside the cabinet, onthe bottom
shelf. The PAsystemcan open the cabinet (whetherthis
emergesfromsomebehaviorin the PAsystemor is initiated
by the TEpassingin a’cabinet door’markerandtelling the
PAsystemto execute an openaction on it, will be determinedby the PAsystem’sbasic capabilities). At this point,
the PAsystemcan instantiate the ’mug’marker.This deictic
representationcan be usedto effectively manipulatethe mug
in later stagesof the plan, suchas pour-coffee-into-mug.
It shouldbe notedthat the PAsystemis not limitedto representing only those objects indicated by the NLScomponent. The PAsystem mayhave its ownroutines actively
looking for task dependentfeatures. Representationacquiredandmaintainedby the PAsystemis referred to as local-spacerepresentation.
Architecture
In the proceedingdiscussionabout our theory of action
andhowit leads to a formof representation,wediscussedan
agent whosecontrol systemconsists of a perception/action
system and someother system componentpossessing nonlocal-spaceinformation.Adiagramof this generalarchitecture appearsin figure 1. Figure2 showsa morestructuredarchitecture, whichour systemuses. Ourarchitectureconsists
of 3 layers, the planningmodule,the task executor,andthe
perception/actionsystem.Thejob of the planningmoduleis
to formulatehigh-levelgoalsfor the agentandcreate plans.
Thetask executorsequencesthese plans to the perception/
action systemas necessary. Theperception/action system
controlsthe agent’seffectors. This layerperformsall actions
andformsall percepts fromthe environment.
In the architectureof figure 2, the meansof integrationbetween the NLScomponentand the PAcomponentis the
Task Executor(TIE). The planning modulehas access
non-local-space representations whichit can incorp~ate
into the plans it generates.TheTEtakes plans andcreates
nninstantiated markersfor each object representedin the
plan. TheseunirLstantiatedmarkersare passedto the PAsys-
139
Figure1. GeneralArchitecture
Figure2. OurArchitecture
temfor instantiation whenthe appropriatestep in the planis
reached.
Thespecificarchitectureof figure 2 is similar to a number
of other 3 layeredarchitectures, suchas (Bonasso&Kortonkamp1995)(Connel11992)(Gat
1992), and is not the subject
of this research.
Features of Integration
Thereare several importantfeaturesof the formof representationthat emerges
fromour theoryof activity. First, our
theory leads to the development
of integrated agentcontrol
systemswith a variable level of autonomy
in their action
sub-systems.Theperception/actionsystemis capableof taking actions basedonly on its ownpercepts and representation, i.e. withoutany informationfromother levels of the
architecture.When
there is little controlinformationflowing
fromthe TEto the PA,the PAis moreautonomous.
In fact,
the PAsystemis capable of completelyautonomous
operation if communicatlon
with the TEis completelycut off. The
systemis then reducedto a Brill-like system.This formof
graceful degradationwasone of Brook’soriginal goals for
subsumption.
Fortasks in whichit is necessaryfor the TEto moretightly control the actions of the PAsystem, the TEcan send
morecontrol informationvia expectants.Somesituations require decisionsthat are morecorrectly madeusing non-local-space information(and therefore morecorrectly made
outside the PAsystem). Decidingwhichwayto go at a Tjunctionis sucha situation. So, ff the agent’splan wasto
navigateto the waterfountainwhichis to the left at a T-junction, whenthe agentarrived at the T-junction,the TEwould
pass an ,minstantiated markercorrespondingto the water
fountain to the PAsystem. The’where’slot of this marker
wouldindicatethat the waterfountainis to the agent’sleft.
In order for the agent to be as robust as possible, the
amountof control exercised over the PAsystemshould be
minimized.
Thisis becauseof the belief that the information
whichthe PAsystem acquires is the most up-to-date and
therefore the mostbelievable. Thenon-local-spaceinformation usedby the planningmodule
is potentially stale. TheTE
communicates
expectantsto the PAsystemas necessary, to
allow the PAsystem to operate moreautonomouslywhen
this is appropriate.
Anotherfeature of this integration methodology
is the
ability to use multiplemapsof differing scale. Planningbecomesmorecomplexfor an agent operating in a large environment. As the numberof features in the environment
grows,so does the complexityof the planningtask. Route
finding and feature selection maywell becomeintractable
for evenmoderatelylarge areas. Consideran agent driving
across the country. Whileon the interstate, manydetails
aboutthe variouslocalities are not needed.Havingmultiple
mapsof differing scale allows,mimportant
details to be abstracted awayfromthe planningprocess.
Thereare also situations whenhavingaccessto twodifferent scale representationsof the samearea is useful. Consider an agent whichis walkingthrougha university campus
in the rain. In order to stay dry, the agententers a near-by
building. Now,the agentstill wishesto reach its original
destination,but in orderto stay dry, the agentwantsto followa route whichstays inside buildingsas muchas possible.
Theagent can use its coarsescale mapof the universityto
formthe context,i.e. the entranceandexit, for the planning
processwhichselects a route througha givenbuildingfrom
the agent’sfmescale mapof that building.
Implementation
In order to decidewhatto do, the PAsystemaccessesits
perceptsandlooksat its markers.Thesestimuli maytrigger
multiple possible behaviorsand the PAsystemmustprioritize its responses.Practical implementation
of this system
requires an expandedviewof markers,fromthat of earlier
markersystems(Agr¢& Chapman
1987)(BriU1995). In
dition to the ’what’and’where’slots, markershave’appearance’, ’action’and’action support’slots.
140
Appearance
Appearance
denotes the sensor signature characteristics
of the object. This involvestwo, not necessarilydistinct,
routinesfor initially locatingthe objectandfor trackingthe
object onceit is marked.Theinitial location routinecan be
a visual routine whichthe agentbeginsexecutingwhenits
PAsystemreceivesan expectant.After the markeris instantinted, the PAsystemcanbeginusingthe trackingroutineto
keepthe marker’sposition up to date.
Action
The’action’ slot is whatthe systemwantsto do with the
markedobject. This is probablywhythe systemmarkedthe
objectin the first place.Theactioncanbe anywhichthe systemknows
aboutfor a particularobject, i.e. for a particular
entry in the marker’s’what’slot. Thepossibleactions will
be determinedby the capabilities of the system.Someactions will be common
to manyobjects. Examplesof common’actions’ include, go-to and get. Examplesof actions
specificto a particularobjectcouldbe ’pour’and’fill’ for a
pitcher. Theagent mayhavea databaseof action routines
whichthe PAsystemcan access.
Progress. Thenotion of task progress is essential to the
agent’s ability to determinewhenan action is complete.
Obviously,the measureof progresswill vary fromtask to
task. Eachaction whichthe systemknowsaboutcan haveits
ownprogressroutine.
Theprogressmayalso feed backinto the action routine.
Forexample,whenpullinga car upto a stop sign, the closer
the vehicle gets to the sign, the morestrongly the breaks
shouldbe applied.
Whilethe PAsystemcan executethe progress routine to
determinehowclose to completionit is on sometask, it may
not be possibleto resolvea lack of progressat the PAsystem
level. ThePAsystemmaybe able to detect that a givenaction is failing, but mayhaveno idea whyor whatelse to try.
At this point, it mustappealto higherlayers of the architecture for guidance.
Action Support
’Action support’ holds information about the action
whichthe perception/actionsystemdoes not possess. This
could be parametersto the action’s progress routine or a
pointer to anothermarkermarkingan object to be"operated" on. Theremayalso be informationapplicableto the particular instance of a task whichthe system is about to
perform.Forexample,if the agentwantedto fall a glass half
full of water froma pitcher, the pitcher wouldbe marked
with a markerwhose’action’ slot contained ’pour’ and
whose’action
support’slot containeda referenceto the glass
marker.Thetask also specifies a stop conditionfor the pour
action, i.e. whenthe glass is half full, whichcanbe checked
by the progressroutine. This conditionalso appearsin the
’action support’slot of the pitcher markerbecauseit is the
pour action that mustbe stopped. Thepour-progressfunction mustbe applied to the object markedby the markerin
the ’actionsupport’slot. i.e. the glass. Anotherexample
of a
parameterto a progress routine mightbe for an open-door
task wherethe doorjust has a handlewhichcan be pulled or
pushed.If the agentknowsthis particular door,it can notify
the progressroutine that it shouldexpecta pushmotionto
causethe doorto openfromthe side the agentis on. If the
progressroutinereports a lack of progress,the agentcan try
pulling
the handle.
System Platform
Weare currentlydevelopinga systembasedon this theory
of action. The platform is a mobile robot based on a
MC6811
CPU.The robot, Bruce, has a single cameraon a
pan/tilt platformfor sensing. Theperception/actionsystem
runs on a workstationsendingcommands
to the robot’s ellectors over a wirelessdata-link. Moredetails andexamples
of the systemin action are available in (Brill, Wasson
Martin1996).
Conclusions
Wehavediscusseda theoryof action whichidentifies actions by associating themwith the objects involved. This
theory encompasses
a representationalsystemand an architectural organizingprinciple. Actionsare performedby acquiringa deictic representationof the necessaryobjectsand
then performingthe action. This theoryleads to a systemof
representationwhichcan expresstask dependentlocal-space
informationandbe usedto integrate perception/actionsystemsand non-local-spaceinformation,via expectants.
Wehavedescribedour minimalistdeictic representation
system,markers.Themarkerinterface is minimalistin both
the numberand size of markers.Uninstantiatedand instantiated markersfacilitate communication
betweena perception/action systemand a systemcomponent
containingnonlocal-space information.Thesetwostates of markersallow
non-local-spaceinformationto be placed into the localspacerepresentationfor a perception/actionsystem.
Thetheoryof actionpresentedin this papercanbe usedto
create systemswhichcan operaterobustlyin large scale domains. Variablelevels of PAsystemautonomyallow rapid
executionof primitive actions whenthe environment
is sufficiently dynamic.Multiplemapsof possiblydiffering scale
can be used to abstract unnecessarydetails awayfromthe
planning process. Ourcommunication
betweensystemcomponents can express non-local-space information at any
scale.
Webelieve that designingagents in accordancewith our
141
theoryof action will producesystemswhichare both sufficient andeffective for operationin large dytlamic environments.
References
[1] Agre,P.E.; and Chapman.
D. 1987. Pengi: AnImplementationof a Theoryof Activity. AAAI-87,268-272.
[2] Attneave,F.; andFarter. P. 1977.TheVisualWorld
Behindthe Head,AmericanJournalof Psychology.Vol.
90, No.4, 549-563.
[3]Brill, EZ.1995.Representationof LocalSpacein Pexception/ActionSystems:BehavingAppropriatelyin Difficult Situations. Ph.D.Dissertation,Department
of
Computer
Science,Universityof Virginia.
[4]Brill, EZ.; Wasson.G.S.; andMartinW.N.1996.Sensing and RepresentingDynamic,Three-Dimensional
Environments:
Judicious Useof RepresentationMeasurably ImprovesPerformance,1ROS-96,submitted.
[5] Brooks, R.A. 1986. A RobustLayeredControl System
for a MobileRobot,IEEEJournalof Roboticsand Automation,RA-2(1):14-23.
[6]Bonasso,R.P.; and Kortenkamp,
D. 1995.Characterizing
an Architecturefor Intelligent, ReactiveAgents.AAA/
Spring Symposium,1995.
[7]Connell,J.H. 1992.SSS:AHybridArchitectureApplied
to RobotNavigation,In Proceedingsof the 1992IEEE
Conferenceon Roboticsand Automation,Nice, France,
2719-2724.
[8]Firby, R.J. 1987.AnInvestigationinto ReactivePlanning
in ComplexDomains,AAAI-87,202-206.
[9] Gat, E. 1992.IntegratingPlanningandReactingin a HeterogeneousAsynchronous
Architecturefor Controlling
Real-WorldMobileRobots, AAAI-92,809-815.
[10] Pylyshyn,Z.W.;and StormR.W.1988. TrackingMultiple Independent
Targets:Evidence
for a Parallel Tracking Mechanism,
Spatial Vision 3(3): 179-197.
[11] Russell,S.J., andNorvig,E1995.Artificial Intelligence: A ModernApproach,Prentice Hall, Englewood
Cliffs, NewJersey.
[12] Tate, A.; Hendler,J.; and Drummond,
M. 1990.A
Reviewof AI PlanningTechniques,Readingsin Planning, Allen, Hendlerand Tate ed., MorganKanfman.
[13] Ullman,S. 1984.VisualRoutines,Cognition18:97159.