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

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“Low Level” Intelligence for “Low Level” Character Animation. “Low level” Animation …?. Animation not having to do with gross body movement or “behavior” Eye gaze Facial expression Ambient / idling animation Animation style Speech? Interesting because an “internal life” is implied.

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

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  1. “Low Level” Intelligence for “Low Level” Character Animation

  2. “Low level” Animation …? • Animation not having to do with gross body movement or “behavior” • Eye gaze • Facial expression • Ambient / idling animation • Animation style • Speech? • Interesting because an “internal life” is implied

  3. Cognitive Modeling CM: Giving characters an internal life Too much autonomy?

  4. “Low level” Cognition …? A class of abilities that are relevant to, but independent of, high-level action • Perception • Knowledge modeling • Attention • Memory • Emotional reaction • Motion quality • …

  5. Example 1: AlphaWolf • Emotional memories: player has total control, but wolves react to instructions based on past experience • Wolves maintain their own cognition, memory and emotion models • B. Tomlinson, “Synthetic Social Relationships for Computational Entities”, PhD. Thesis, MIT Media Lab 2002

  6. Example 2: Object Persistence • Piaget: The persistence of a mental image after the sensory stimulus has been removed • Object Persistence = location expectation formation • Focus on search tasks (where do I expect the sheep to be?)

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

  8. 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 Observations can have emotional impact • Wanted to see something but didn’t  confusion • Saw something where you didn’t expect it to be  surprise • Having trouble finding the target  frustration … plus variations • Target desired + confusion  disappointment • Target feared + surprise  panic • Target desired + surprise  delight Emotions may • Focus attention (salience) • Bias behavioral choices / Affect decision-making parameters • Affect animation (facial and parameterized) • Act as a debugging channel!

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

  12. Results: Duncan the Highland Terrier Duncan: • Virtual sheep-herding • Layered behavior system • Synthetic vision Results: • Emergent look-around • Emergent search • Salient Moving objects • Distribution-based object-mapping • Emotional reactions • Surprise • Confusion • Frustration Video

  13. Conclusions • “Low Level” Conclusions • A model of Object Persistence • Simple mechanism, complex results • Simple implementation • Intuitive • “High Level” Conclusion • Intelligence >> Action-selection • You control the wolves, but what they feel matters • You control Duncan, but what he knows matters

  14. Damián 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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