Presentation on theme: "Object Persistence for Synthetic Characters Damian Isla Bungie Studios Microsoft Corp. Bruce Blumberg Synthetic Characters MIT Media Lab."— Presentation transcript:
Object Persistence for Synthetic Characters Damian Isla Bungie Studios Microsoft Corp. Bruce Blumberg Synthetic Characters MIT Media Lab
Expectations for Synthetic Creatures Expectations: Assumed aspect of world state that – for one reason or another – cannot be observed directly Assertion: The ability to form expectations and act on them is an essential component of common sense intelligence. Learning Gradual, long time-scales, large example sets e.g. learn to classify spoken utterances Expectations Immediate, short time-scale, small example sets e.g. Sheep walks behind a wall. Where did it go? When will I see it again?
Object Persistence Object persistence as Location Expectation When a target objects location is not observed for some time, how is the creatures idea of the location maintained / updated?
The Domain Duncan Duncan Concentrate on search tasks Concentrate on search tasks
Probabilistic Framework Usually a space of predictions Usually a space of predictions Negative verification: space of negated predictions Negative verification: space of negated predictions Distribution representation is key Distribution representation is key
Spatial Expectations Probabilistic Occupancy Map –Discrete spatial probability distribution –Uncertainty through discrete diffusion
POM Algorithm If target observed:Find closest node n* Otherwise:Divide map nodes into visible (V) and nonvisible (N) sets Either way:Diffuse Probability Positive Verification Unverifiable Negative Verification
Emergent Look-Around Also: Emergent Search Also: Emergent Search Simple rule: always direct gaze towards most likely location of the target Simple rule: always direct gaze towards most likely location of the target
Expectations and Emotions Many emotions imply expectations Many emotions imply expectations –Surprise, disappointment, satisfaction, confusion, dread, anticipation… Individual observations may have affective implications Individual observations may have affective implications Emotional autonomic variables: Emotional autonomic variables: Emotions may –Focus attention (salience) –Bias behavioral choices –Affect decision-making parameters –Affect animation (facial and parameterized) –Act as indicators of overall system state
Expectations and Emotions Surprise (unexpected observation ) Surprise (unexpected observation ) Confusion (negated expectation) Confusion (negated expectation) –Proportional to amount of culled probability Frustration (consistently negated expectations) Frustration (consistently negated expectations)