| Our understanding of social insect behavior has significantly influenced artificial intelligence (AI) and multirobot systems' research (e.g., ant algorithms and swarm robotics). In this work, however, we focus on the opposite question: BHow can multirobot systems research contribute to the understanding of social animal behavior?[ As we show, we are able to contribute at several levels. First, using algorithms that originated in the robotics community, we can track animals under observation

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... provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. In some cases the automatic labeling is more accurate and consistent than manual behavior identification. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An execut-able model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multirobot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects. . His research focuses on how the complex behavior of ant and bee colonies emerges from the actions and interactions of colony members, without direction from a wellinformed central controller.