Behavior selection and learning for synthetic character

This paper proposes a behavior selection mechanism and interactive learning method for a synthetic character. Synthetic character can decide its behavior by itself based on its own internal states (motivation, homeostasis, and emotion), and external sensor information. A behavior is selected by both probabilistic and deterministic methods. The probabilistic method uses the internal states and external sensor information, and the deterministic method which imitates animal´s instinct, uses only external sensor information. Both methods are complementary to each other. A user can teach the synthetic character a desired behavior among many behaviors, and behaviors are grouped into analogous behavior sets. The learning algorithm includes the emotional parameters by which the training efficiency is affected. The performance of the synthetic character, Rity, developed with the proposed mechanism at RIT Lab., KAIST is demonstrated in a 3D virtual world.