Abstract
Interactive behavior of biological agents represents an important area in life as we know it. Behavior matching and imitation may serve as fundamental mechanisms for the development of societies and individuals. Imitation and
observational learning as means for acquiring new behaviors also represent a largely untapped resource for robotics and artificial life — both in the study of life as it could be and for applications of biological tricks to synthetic worlds. This paper describes a new general imitating mechanism called ALICE
(Action Learning for Imitation via Correspondences between Embodiments) that addresses the important correspondence problem in imitation. The mechanism is implemented and illustrated on the chessworld test-bed that was used in previous work to address the effects of agent embodiment, metrics and
granularity when learning how to imitate another. The performance of the imitating agent is shown to improve when ALICE is complementing its imitation behavior generating mechanism.
observational learning as means for acquiring new behaviors also represent a largely untapped resource for robotics and artificial life — both in the study of life as it could be and for applications of biological tricks to synthetic worlds. This paper describes a new general imitating mechanism called ALICE
(Action Learning for Imitation via Correspondences between Embodiments) that addresses the important correspondence problem in imitation. The mechanism is implemented and illustrated on the chessworld test-bed that was used in previous work to address the effects of agent embodiment, metrics and
granularity when learning how to imitate another. The performance of the imitating agent is shown to improve when ALICE is complementing its imitation behavior generating mechanism.
| Original language | English |
|---|---|
| Journal | Proceedings of the First International Workshop on Epigenetic Robotics |
| Publication status | Published - 2001 |