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Object Persistence for Synthetic Characters

Object Persistence for Synthetic Characters. Expectations for Synthetic Creatures. Expectations: Assumed aspect of world state that – for one reason or another – cannot be observed directly

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Object Persistence for Synthetic Characters

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  1. Object Persistence for Synthetic Characters

  2. 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. LearningGradual, long time-scales, large example setse.g. learn to classify spoken utterances ExpectationsImmediate, short time-scale, smallexample setse.g. Sheep walks behind a wall. Where did it go? When will I see it again?

  3. Object Persistence Object persistence as Location Expectation When a target object’s location is not observed for some time, how is the creature’s idea of the location maintained / updated?

  4. The Domain • Duncan • Concentrate on search tasks

  5. Expectation Theory • Observation + Predictor  Expectation • Expectation  Verification • Positive verification (confirmation) • Negative verification (expectation violation) • Unverifiable • Verification  Expectation refinement • Possibly also predictor refinement

  6. Probabilistic Framework • Usually a space of predictions • Negative verification: space of negated predictions • Distribution representation is key

  7. Spatial Expectations Probabilistic Occupancy Map • Discrete spatial probability distribution • Uncertainty through discrete diffusion

  8. Positive Verification Unverifiable Negative Verification POM Algorithm If target observed: Find closest node n* Otherwise: Divide map nodes into visible (V) and nonvisible (N) sets Either way: Diffuse Probability

  9. Emergent Look-Around • Simple rule: always direct gaze towards most likely location of the target • Also: Emergent Search

  10. Expectations and Emotions • Many emotions imply expectations • Surprise, disappointment, satisfaction, confusion, dread, anticipation… • Individual observations may have affective implications • 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

  11. Expectations and Emotions • Surprise (unexpected observation) • Confusion (negated expectation) • Proportional to amount of culled probability • Frustration (consistently negated expectations)

  12. Architecture • Synthetic vision • Rule-matching • Parameterized animation engine • Burke et al., CreatureSmarts, GDC 2001

  13. Results • Emergent look-around • Emergent search • Salient Moving objects • Distribution-based object-mapping • Emotional reactions • Surprise • Confusion • Frustration Video

  14. Issue: Scalability • Adaptive resolution maps • Logical maps • Hierarchical maps

  15. Conclusions • Simple mechanism, complex results • Simple implementation • Intuitive • Layered decision-making • Pseudo-reasoning • Useful theory

  16. Damian Isla naimad@media.mit.edu http://www.media.mit.edu/~naimad Bruce Blumberg bruce@media.mit.edu http://www.media.mit.edu/~bruce Questions? Synthetic Characters http://www.media.mit.edu/characters

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