Imitation learning is a promising route to instruct robotic multi-agent systems. However, imitating agents should be able to decide autonomously what behavior, observed in others, is interesting to copy. Here we investigate whether a simple recurrent network (Elman Net) can be used to extract meaningful chunks from a continuous sequence of observed actions. Results suggest that, even in spite of the high level of task specific noise, Elman nets can be used for isolating re-occurring action patterns in robots. Limitations and future directions are discussed. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Vanderelst, D., & Barakova, E. (2008). Autonomous parsing of behavior in a multi-agent setting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5097 LNAI, pp. 1198–1209). https://doi.org/10.1007/978-3-540-69731-2_112
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