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 Islanaimad@media.mit.edunaimad@media.mit.edu

http://www.media.mit.edu/http://www.media.mit.edu/~naimad~naimad

Bruce BlumbergBruce Blumbergbruce@media.mit.edubruce@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