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Intelligent Behaviors for Simulated Entities
Presented by: Ryan HouletteStottler Henke Associates, [email protected]
Jeremy LudwigStottler Henke Associates, [email protected]
I/ITSEC 2006 Tutorial
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OutlineDefining “intelligent behavior”Authoring methodologyTechnologies:
• Cognitive architectures• Behavioral approaches• Hybrid approaches
ConclusionQuestions
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The GoalIntelligent behavior
• a.k.a. entities acting autonomously• generally replacements for humans
– when humans are not available• scheduling issues• location• shortage of necessary expertise• simply not enough people
– when humans are too costly Defining Intelligent
Behavior Authoring Methodology Technologies: Cognitive Architectures Behavioral Approaches Hybrid Approaches Conclusion
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“Intelligent Behavior”• Pretty vague!• General human-level AI not yet possible
– computationally expensive– knowledge authoring bottleneck
• Must pick your battles– what is most important for your application– what resources are available
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Decision Factors1
Entity “skill set”FidelityAutonomyScalabilityAuthoring
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Factor: Entity Skill SetWhat does the entity need to be able to do?
– follow a path– work with a team– perceive its environment– communicate with humans– exhibit emotion/social skills– etc.
Depends on purpose of simulation, type of scenario, echelon of entity
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Factor: FidelityHow accurate does the entity’s behavior need to
be?– correct execution of a task– correct selection of tasks– correct timing– variability/predictability
Again, depends on purpose of simulation and echelon• training => believability• analysis => correctness
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Factor: AutonomyHow much direction does the entity need?
– explicitly scripted– tactical objectives– strategic objectives
Behavior reusable across scenariosDynamic behavior => less brittle
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Factor: ScalabilityHow many entities are needed?
– computational overhead– knowledge/behavior authoring costs
Can be mitigated• aggregating entities• distributing entities
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Factor: AuthoringWho is authoring the behaviors?
– programmers– knowledge engineers– subject matter experts– end users / soldiers
Training/skills required for authoringQuality of authoring toolsEase of modifying/extending behaviors
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Choosing an Approach
Also ease of integration with simulation....
Skill SetFidelity
Autonomy
ScalabilityEase of Authoring
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Agent TechnologiesWide range of possible approachesWill discuss the two extremes
CognitiveArchitectures
BehavioralApproaches
deliberative reactive
EPIC, ACT-R, Soar scriptingFSMs
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Authoring Methodologies
Simulation
AgentArchitecture
BehaviorModel
Defining Intelligent Behavior
Authoring Methodology Technologies: Cognitive Architectures Behavioral Approaches Hybrid Approaches Conclusion
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Basic Authoring Procedure
Determinedesired
behavior
Buildbehavior
model
DONEDONE!
Runsimulation
Evaluateentity
behavior
Refine behavior model
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Iterative AuthoringOften useful to start with limited set of behaviors
• particularly when learning new architecture• depth-first vs. breadth-first
Test early and oftenBuild initial model with revision in mind
• good software design principles apply: modularity, encapsulation, loose coupling
Determining why model behaved incorrectly can be difficult• some tools can help provide insight
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The Knowledge BottleneckModel builder is not subject matter expertTransferring knowledge is labor-intensive
• For TacAir-Soar, 70-90% of model dev. time
To reduce the bottleneck:• Repurpose existing models• Use SME-friendly modeling tools• Train SMEs in modeling skills
=> Still an unsolved problem
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The Simulation InterfaceSimulation sets bounds of behavior
• the primitive actions entities can perform• the information about the world that is available to entities
Can be useful to “move interface up”• if simulation interface is too low-level• abstract away simulation details
– in wrapper around agent architecture– in “library” within the behavior model itself
• enables behavior model to be in terms of meaningful units of behavior
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Cognitive ArchitecturesOverviewEPIC, ACT-R, & SoarExamples of Cognitive ModelsStrengths / Weakness of Cognitive
Architectures
Defining Intelligent Behavior
Authoring Methodology Technologies: Cognitive Architectures Behavioral Approaches Hybrid Approaches Conclusion
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IntroductionWhat is a cognitive architecture?
• “a broad theory of human cognition based on a wide selection of human experimental data and implemented as a running computer simulation” (Byrne, 2003)
Why cognitive architectures?• Advance psychological theories of cognition• Create accurate simulations of human behavior
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IntroductionWhat is cognition?Where does psychology fit in?
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Cognitive Architecture Components
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A Theory – The Model Human ProcessorSome principles of operation
• Recognize-act cycle• Fitt’s law• Power law of practice• Rationality principle• Problem space principle
(from Card, Moran, & Newell, 1983)
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ArchitectureDefinition
• “a broad theory of human cognition based on a wide selection of human experimental data and implemented as a running computer simulation” (Byrne, 2003)
Two main components in modeling• Cognitive model programming language• Runtime Interpreter
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EPIC ArchitectureProcessors
• Cognitive• Perceptual• Motor
Operators• Cognitive• Perceptual• Motor• Knowledge
Representation
(from Kieras, http://www.eecs.umich.edu/ ~kieras/epic.html)
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Model
ArchitectureRuntime
ArchitectureLanguage
Task Environment
Task Description
Task Strategy
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Task DescriptionThere are two points on the screen:
A and B. The task is to point to A with the right hand, and
press the “Z” key with the left hand when it is reached.
Then point from A to B with the right hand and press the “Z” key with the left hand.
Finally point back to A again, and press the “Z” key again.
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Task Environment
A B
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Task Strategy –EPIC Production Rules
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EPIC Production Rule(Top_point_AIF( (Step Point AtA)
(Motor Manual Modality Free)(Motor Ocular Modality Free)(Visual ?object Text My_Point_A)
)THEN(
(Send_to_motor Manual Perform Ply Cursor ?object Right)(Delete (Step Point AtA))(Add (Step Click AtA))
))
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ACT-R and SoarMotivationsFeaturesModels
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Initial MotivationsACT-R
• Memory • Problem solving
Soar• Learning• Problem solving
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ACT-R Architecture
(from Bidiu, R., http://actr.psy.cmu.edu/about/)
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Some ACT-R FeaturesDeclarative memory stored in chunks
• Memory activation• Buffer sizes between modules is one chunk
One rule per cycleLearning
• Memory retrieval, production utilities• New productions, new chunks
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ACT-R 6.0 IDE
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Task DescriptionSimple Addition
• 1 + 3 = 4• 2 + 2 = 4
Goal: mimic the performance of four year olds on simple addition tasks
• This is a memory retrieval task, where each number is retreived (e.g. 1 and 3) and then an addition fact is retrieved (1 + 3 = 4)
• The task demonstrates partial matching of declarative memory items, and requires tweaking a number of parameters.
From the ACT-R tutorial, Unit 6
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ACT-R 6.0 Production Rules(p retrieve-first-number
=goal>isa problemarg1 =onestate nil
==>=goal>state encoding-one+retrieval>isa numbername =one
)
(p encode-first-number=goal>isa problemstate encoding-one=retrieval>isa number
==>=goal>state retrieve-twoarg1 =retrieval
)
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Some Relevant ACT-R Models• Best, B., Lebiere, C., & Scarpinatto, C. (2002). A model of
synthetic opponents in MOUT training simulations using the ACT-R cognitive architecture. In Proceedings of the Eleventh Conference on Computer Generated Forces and Behavior Representation. Orlando, FL.
• Craig, K., Doyal, J., Brett, B., Lebiere, C., Biefeld, E., & Martin, E. (2002). Development of a hybrid model of tactical fighter pilot behavior using IMPRINT task network model and ACT-R. In Proceedings of the Eleventh Conference on Computer Generated Forces and Behavior Representation. Orlando, FL
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Soar ArchitectureProblem Space Based
State1Attribute1: Value1
State2Attribute1:Value2
State3Attribute1:Value1
State4Attribute1:Value3
Attribute2: true
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Some Soar FeaturesProblem space based
• Attribute/value hierarchy (WM) forms the current state• Productions (LTM) transform the current state to achieve
goals by applying operatorsCycle
• Input• Elaborations fired• All possible operators proposed• One selected• Operator applied• Output
Impasses & Learning
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Soar 8.6.2 IDE
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Task DescriptionControl the behavior of a Tank on the game board.
• Each tank has a number of sensors (e.g. radar) to find enemies, missiles to launch at enemies, and limited resources
From the Soar Tutorial
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Propose Movessp {propose*move (state <s> ^name wander ^io.input-link.blocked.forward no)--> (<s> ^operator <o> +) (<o> ^name move ^actions.move.direction forward)}
sp {propose*turn (state <s> ^name wander ^io.input-link.blocked <b>) (<b> ^forward yes ^ { << left right >> <direction> } no)--> (<s> ^operator <o> + =) (<o> ^name turn ^actions <a>) (<a> ^rotate.direction <direction> ^radar.switch on ^radar-power.setting 13)}
sp {propose*turn*backward (state <s> ^name wander ^io.input-link.blocked <b>) (<b> ^forward yes ^left yes ^right yes)--> (<s> ^operator <o> +) (<o> ^name turn ^actions.rotate.direction left)}
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Prefer Movessp {select*radar-off*move
(state <s> ^name wander^operator <o1> +^operator <o2> +)(<o1> ^name radar-off)(<o2> ^name << turn move >>)
-->(<s> ^operator <o1> > <o2>)
}
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Apply Movesp {apply*move (state <s> ^operator <o> ^io.output-link <out>) (<o> ^direction <direction> ^name move)--> (<out> ^move.direction <direction>)}
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Elaborationssp {elaborate*state*missiles*low (state <s> ^name tanksoar ^io.input-link.missiles 0)--> (<s> ^missiles-energy low)}
sp {elaborate*state*energy*low (state <s> ^name tanksoar ^io.input-link.energy <= 200)--> (<s> ^missiles-energy low)}
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Some Relevant Soar Models• Wray, R.E., Laird, J.E., Nuxoll, A., Stokes, D., Kerfoot, A.
(2005). Synthetic adversaries for urban combat training. AI Magazine, 26(3):82-92.
• Jones, R. M., Laird, J. E., Nielsen, P. E., Coulter, K. J., Kenny, P., & Koss, F. V. (1999). Automated intelligent pilots for combat flight simulation. AI Magazine, 20(1), 27-41.
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Strengths / Weaknesses of Cognitive Architectures
Strengths• Supports aspects of intelligent behavior, such as learning,
memory, and problem solving, not supported by other types of architectures
• Can be used to accurately model human behavior, especially human-computer interaction, at small grain sizes (measured in ms)
Weaknesses• Can be difficult to author, modify, and debug complicated
sets of production rules– High level modeling languages (e.g. CogTool, Herbal, High
Level Symbolic Representation language)– Automated model generation (e.g. Konik & Laird, 2006)
• Computational issues when scaling to large number of entities
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Behavioral ApproachesFocus is on externally-observable behavior
• no explicit modeling of knowledge/cognition• instead, behavior is explicitly specified:
“Go to destination X, then attack enemy.”
Often a natural mapping from doctrine to behavior specifications
Defining Intelligent Behavior
Authoring Methodology Technologies: Cognitive Architectures Behavioral Approaches Hybrid Approaches Conclusion
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Hard-coding BehaviorsSimplest approach is write behavior in C++/Java:
MoveTo(location_X);AcquireTarget(target);FireAt(target);
Don’t do this!• Can only be modified by programmers• Hard to update and extend• Behavior models not easily portable
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Scripting BehaviorsWrite behaviors in scripting language
– UnrealScript
Avoids many problems of hard-coding• not tightly coupled to simulation code• more portable• often simplified to be easier to learn & use
Fine for linear sequences of actions, but do not scale well to complex behavior
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Finite State Machine (FSM)Specifies a sequence of decisions and actionsBasic form is essentially a flowchart
X?
Z?
yes
no
yes
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An FSM ExampleA basic Patrol behavior
• implemented for bots in Counter-Strike• built in SimBionic visual editor
Simulation interface• Primitive actions:
– FollowPath, TurnTo, Shoot, Reload
• Sensory inputs:– AtDestination, Hear, SeeEnemy, OutOfAmmo, IsDead
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An FSM Example (2)
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An FSM Example (3)
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An FSM Example (4)
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FSMs: AdvantagesVery commonly-used techniqueEasy to implementEfficientIntuitive visual representation
• Accessible to SMEs• Maintainable
Variety of tools available
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FSMs: DisadvantagesHave difficulty accurately modeling:
• behavior at small grain sizes• human-entity interaction
Lack of planning and learning capabilities=> brittleness (can’t cope with situations unforeseen by the modeler)
Tend to scale ungracefully
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Hierarchical FSMsAn FSM can delegate to another FSM
• SearchBuilding ClearRoom
Allows modularization of behaviorReduces complexityEncourages reuse of model components
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Hierarchical FSMs (2)
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Hierarchical FSMs (3)
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Hybrid ArchitecturesCombine cognitive approaches
– EASE: Elements of ACT-R, EPIC, & Soar (Chong & Wray, 2005)
Combine behavioral and cognitive approaches– Imprint / ACT-R (Craig, et al., 2002)– SimBionic / Soar
Defining Intelligent Behavior
Authoring Methodology Technologies: Cognitive Architectures Behavioral Approaches Hybrid Approaches Conclusion
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Hybrid ArchitecturesCombine cognitive & behavioral approaches
Pros:• More scalable• Easier to author• More flexible
Cons:• Architecture is more complex
Cognitive Layer
Behavioral Layer
Simulation
behaviors
goals
actions
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Hybrid Example: HTN Planner + FSMsHierarchical Task Network (HTN) Planner• Inputs:
• goals• library of plan fragments (HTNs)
• Outputs: • High-level plan achieving those goals
– Each plan step is an FSM in the Behavior Layer
• Not really a cognitive architecture, but adds goal-driven capability to system• Plan fragments represent codified sequences of
behavior
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ConclusionFactors affecting choice of architecture:
• Entity capabilities• Behavior fidelity• Level of autonomy• Number of entities• Authoring resources
Two main paradigms:• cognitive architectures• behavioral approaches
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Conclusion (2)Recommend iterative model development
• Build• Test• Refine
Be aware of the knowledge bottleneck
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ResourcesEPIC
http://www.eecs.umich.edu/~kieras/epic.htmlACT-R
http://act-r.psy.cmu.edu/SOAR
http://sitemaker.umich.edu/soarSimBionic
http://www.simbionic.com/
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Questions?
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