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Object Persistence for Object Persistence for Synthetic CharactersSynthetic Characters
Damian IslaDamian IslaBungie StudiosBungie Studios
Microsoft Corp.Microsoft Corp.
Bruce BlumbergBruce BlumbergSynthetic Synthetic
CharactersCharacters
MIT Media LabMIT Media Lab
Expectations for Synthetic Expectations for Synthetic CreaturesCreaturesExpectations: Expectations:
Assumed aspect of world state that – for one reason or Assumed aspect of world state that – for one reason or another – cannot be observed directlyanother – cannot be observed directly
Assertion: Assertion: The ability to form expectations and act on them is an The ability to form expectations and act on them is an essential component of common sense intelligence.essential component of common sense intelligence.
LearningLearningGradual, long time-scales, large example setsGradual, long time-scales, large example setse.g. learn to classify spoken utterancese.g. learn to classify spoken utterances
ExpectationsExpectationsImmediate, short time-scale, smallImmediate, short time-scale, small example setsexample setse.g. Sheep walks behind a wall. Where did it go? When will I see it again?e.g. Sheep walks behind a wall. Where did it go? When will I see it again?
Object PersistenceObject Persistence
Object persistence as Object persistence as Location ExpectationLocation Expectation
When a target object’s location is not observed for When a target object’s location is not observed for some time, how is the creature’s idea of the some time, how is the creature’s idea of the location maintained / updated?location maintained / updated?
The DomainThe Domain
DuncanDuncan
Concentrate on Concentrate on search taskssearch tasks
Expectation TheoryExpectation Theory
Observation + Predictor Observation + Predictor Expectation Expectation
Expectation Expectation Verification Verification– Positive verification (confirmation)Positive verification (confirmation)– Negative verification (expectation violation)Negative verification (expectation violation)– UnverifiableUnverifiable
Verification Verification Expectation refinement Expectation refinement– Possibly also predictor refinementPossibly also predictor refinement
Probabilistic FrameworkProbabilistic Framework
Usually a Usually a spacespace of of predictionspredictions
Negative verification: space Negative verification: space of negated predictionsof negated predictions
Distribution representation Distribution representation is keyis key
p(x)
x
p(x)
x
x
p(x)
Spatial ExpectationsSpatial Expectations
Probabilistic Occupancy MapProbabilistic Occupancy Map– Discrete spatial probability distributionDiscrete spatial probability distribution– Uncertainty through discrete diffusionUncertainty through discrete diffusion
POM AlgorithmPOM Algorithm
If target observed:If target observed: Find closest node n*Find closest node n*
Otherwise:Otherwise: Divide map Divide map nodes into visible (V) and nonvisible (N) nodes into visible (V) and nonvisible (N) setssets
Either way:Either way: Diffuse ProbabilityDiffuse Probability
*0, ( )n n p n
*1( )p n
( )culled
n V
p p n
0, ( )n V p n
1, ( ) ( )
1culled
n N p n p np
PositiveVerification
Unverifiable
NegativeVerification
Emergent Look-AroundEmergent Look-Around
Also: Emergent SearchAlso: Emergent Search
Simple rule: always direct gaze towards most likely location Simple rule: always direct gaze towards most likely location of the targetof the target
Expectations and EmotionsExpectations and Emotions
Many emotions imply expectationsMany emotions imply expectations– Surprise, disappointment, satisfaction, confusion, dread, Surprise, disappointment, satisfaction, confusion, dread,
anticipation…anticipation…
Individual observations may have affective implications Individual observations may have affective implications
Emotional autonomic variables:Emotional autonomic variables:
Emotions mayEmotions may– Focus attention (salience)Focus attention (salience)– Bias behavioral choicesBias behavioral choices– Affect decision-making parametersAffect decision-making parameters– Affect animation (facial and parameterized)Affect animation (facial and parameterized)– Act as indicators of overall system stateAct as indicators of overall system state
1t t tinstx x x
Expectations and EmotionsExpectations and Emotions
Surprise (unexpected observationSurprise (unexpected observation))
Confusion (negated expectation)Confusion (negated expectation)– Proportional to amount of culled Proportional to amount of culled
probabilityprobability
Frustration (consistently negated Frustration (consistently negated expectations)expectations)
*
*
)
)
(
(
highesttinst
p p ns
p n
( )tinst culled
n V
c p p n
t tinstf kc
Time
Confusion FrustrationSurprise
Duncan instructed to approach sheep
Discovers sheep is notin last-observed location
Sheep found inunexpected location.
Va
ria
ble
Va
lue
ArchitectureArchitecture
Action-Selection
Navigation
MotorSystem
Target 1
Target 2
Working Memory
Spatial System
Play animation “run”
When I hear “sick ‘em”, approach sheep
When I hear “come”, approach shepherd
When I hear “stay”, lay down
If I need to approach something, run with bearing x
Otherwise, just look at the most salient thing
Synthetic visionSynthetic vision Rule-matchingRule-matching Parameterized animation engineParameterized animation engine
Burke et al., Burke et al., CreatureSmarts,CreatureSmarts, GDC 2001 GDC 2001
ResultsResults
Emergent look-aroundEmergent look-around Emergent searchEmergent search Salient Moving objectsSalient Moving objects Distribution-based object-Distribution-based object-
mappingmapping Emotional reactionsEmotional reactions
– SurpriseSurprise– ConfusionConfusion– FrustrationFrustration
VideoVideo
Issue: ScalabilityIssue: Scalability
Adaptive resolution mapsAdaptive resolution maps
Logical mapsLogical maps
Hierarchical mapsHierarchical maps
Nearthe
door
Middleof theroom
Left of thedesk
Behind thedesk
Right ofthe desk
Along the wall
Alo
ng
th
e w
all
the deskand the
chair
Between
Over there
ConclusionsConclusions
Simple mechanism, complex resultsSimple mechanism, complex results– Simple implementationSimple implementation– IntuitiveIntuitive
Layered decision-makingLayered decision-making– Pseudo-reasoningPseudo-reasoning
Useful theoryUseful theory
Questions?Questions?
Damian IslaDamian [email protected]@media.mit.edu
http://www.media.mit.edu/http://www.media.mit.edu/~naimad~naimad
Bruce BlumbergBruce [email protected]@media.mit.edu
http://www.media.mit.edu/http://www.media.mit.edu/~bruce~bruce
Synthetic CharactersSynthetic Charactershttp://www.media.mit.edu/charactershttp://www.media.mit.edu/characters