(Georgia Institute of Technology, 2006-07)
Balch, Tucker; Dellaert, Frank; Feldman, Adam; Guillory, Andrew; Isbell, Charles L.; Khan, Zia; Pratt, Stephen; Stein, Andrew; Wilde, Hank
Our understanding of social insect behavior has significantly influenced A.I. and multi-robot
systems’ research (e.g. ant algorithms and swarm robotics). In this work, however, we focus
on the opposite question, namely: “how can multi-robot 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 to 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. Our ultimate goal, however,
is to automatically create, from observation, executable models of behavior. An executable 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 multi-robot 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.