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Synthetic Characters' Object Persistence and Expectations Theory

Understanding object persistence and expectations in synthetic creatures. Learn about spatial expectations, emotional implications, and the probabilistic framework. Explore how synthetic characters form, refine, and act on expectations. Discover the implications for their behavior and decision-making process.

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Synthetic Characters' Object Persistence and Expectations Theory

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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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