““Low Level” Intelligence for Low Level” Intelligence for “Low Level” Character “Low Level” Character

AnimationAnimation

DamiDamiáán Islan IslaBungie StudiosBungie Studios

Microsoft Corp.Microsoft Corp.

Bruce BlumbergBruce BlumbergSynthetic Synthetic

CharactersCharacters

MIT Media LabMIT Media Lab

““Low level” Animation …?Low level” Animation …?

Animation not having to do with gross Animation not having to do with gross body movement or “behavior”body movement or “behavior”– Eye gazeEye gaze– Facial expressionFacial expression– Ambient / idling animationAmbient / idling animation– Animation styleAnimation style– Speech?Speech?

Interesting because an “internal life” is Interesting because an “internal life” is impliedimplied

Cognitive ModelingCognitive Modeling

CM: Giving characters an internal lifeCM: Giving characters an internal life

Too much autonomy?Too much autonomy?

ProsProsUnpredictabilityUnpredictabilityResponsivenessResponsiveness

Leverage animationLeverage animation

ConsConsUnpredictabilityUnpredictabilityReproducibilityReproducibilityControllabilityControllability

““Low level” Cognition …?Low level” Cognition …?

A class of abilities that are relevant to, but A class of abilities that are relevant to, but independent of, high-level independent of, high-level actionaction

PerceptionPerception Knowledge modelingKnowledge modeling AttentionAttention MemoryMemory Emotional reactionEmotional reaction Motion qualityMotion quality ……

Perception

Memory AttentionKnowledge

Base

ActionSelection

Emotions

Gross BodyAnimation

FacialAnimation

GazeControl

UserControl

Example 1: AlphaWolfExample 1: AlphaWolf

Emotional memories: player Emotional memories: player has total control, but wolves has total control, but wolves react to instructions based on react to instructions based on past experiencepast experience

B. Tomlinson, “Synthetic Social B. Tomlinson, “Synthetic Social Relationships for Relationships for Computational Entities”, PhD. Computational Entities”, PhD. Thesis, MIT Media Lab 2002Thesis, MIT Media Lab 2002

Wolves maintain Wolves maintain their own their own cognition, cognition, memory and memory and emotion modelsemotion models

Example 2: Object PersistenceExample 2: Object Persistence

Piaget: The persistence of a mental image after the Piaget: The persistence of a mental image after the sensory stimulus has been removedsensory stimulus has been removed

Object Persistence = Object Persistence = location expectation location expectation formationformation

Focus on search tasks (where do I expect the sheep Focus on search tasks (where do I expect the sheep to be?)to be?)

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

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

Observations can have emotional impactObservations can have emotional impact– Wanted to see something but didn’t Wanted to see something but didn’t confusion confusion– Saw something where you didn’t expect it to be Saw something where you didn’t expect it to be surprise surprise– Having trouble finding the target Having trouble finding the target frustration frustration

… … plus variationsplus variations– Target desired + confusion Target desired + confusion disappointment disappointment– Target feared + surprise Target feared + surprise panic panic– Target desired + surprise Target desired + surprise delight delight

Emotions mayEmotions may– Focus attention (salience)Focus attention (salience)– Bias behavioral choices / Affect decision-making parametersBias behavioral choices / Affect decision-making parameters– Affect animation (facial and parameterized)Affect animation (facial and parameterized)– Act as a debugging channel!Act as a debugging channel!

Expectations and EmotionsExpectations and Emotions

Emotional Autonomic variableEmotional Autonomic variable

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

1t t tinstx x x

Results: Duncan the Highland Results: Duncan the Highland TerrierTerrierDuncan:Duncan: Virtual sheep-herdingVirtual sheep-herding Layered behavior systemLayered behavior system Synthetic visionSynthetic vision

Results:Results: 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

ConclusionsConclusions

““Low Level” ConclusionsLow Level” Conclusions– A model of Object PersistenceA model of Object Persistence– Simple mechanism, complex resultsSimple mechanism, complex results

Simple implementationSimple implementation IntuitiveIntuitive

““High Level” ConclusionHigh Level” Conclusion– Intelligence >> Action-selectionIntelligence >> Action-selection

You control the wolves, but what they feel mattersYou control the wolves, but what they feel matters You control Duncan, but what he knows mattersYou control Duncan, but what he knows matters

Questions?Questions?

DamiDamiáán Islan 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