Learning in behavior-based multi-robot systems: policies, models, and other agents

Authors:
Maja J. Matarić
Affiliations:
Computer Science Department, University of Southern California, 941 West 37th Place, Mailcode 0781, Los Angeles, CA 90089-0781, USA
Venue:
Cognitive Systems Research
Year:
2001

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Abstract

This paper describes how the use of behaviors as the underlying control representation provides a useful encoding that both lends robustness to control and allows abstraction for handling scaling in learning, focusing on multi-agent/robot systems. We first define situatedness and embodiment, two key concepts in behavior-based systems (BBS), and then define BBS in detail and contrast it with alternatives, namely reactive, deliberative, and hybrid control. The paper ten focuses on the role and power of behaviors as a representational substrate in learning policies and models, as well as learning from other agents (by demonstration and imitation). We overview a variety of methods we have demonstrated for learning in the multi-robot problem domain.