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Proceedings Paper

Multirobot learning in an inherently cooperative task
Author(s): Lynne E. Parker; Claude Touzet
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Paper Abstract

An important need in multi-robot systems is the development of mechanisms that enable robot teams to autonomously generate cooperative behaviors. This paper first briefly presents the Cooperative Multi-robot Observation of Multiple Moving Targets (CMOMMT) application as a rich domain for studying the issues of multi-robot learning of new behaviors. We discuss the results of our hand-generated algorithm for CMOMMT, and then describe our research in generating multi-robot learning techniques for the CMOMMT application, comparing the results to the hand-generated solutions. Our results show that, while the learning approach performs better than random, naive approaches, much room still remains to match the results obtained from the hand-generated approach. The ultimate goal of this research is to develop techniques for multi-robot learning and adaptation that will generalize to cooperative robot applications in many domains, thus facilitating the practical use of multi-robot teams in a wide variety of real-world applications.

Paper Details

Date Published: 20 September 2001
PDF: 9 pages
Proc. SPIE 4364, Unmanned Ground Vehicle Technology III, (20 September 2001); doi: 10.1117/12.439972
Show Author Affiliations
Lynne E. Parker, Oak Ridge National Lab. (United States)
Claude Touzet, Oak Ridge National Lab. (United States)

Published in SPIE Proceedings Vol. 4364:
Unmanned Ground Vehicle Technology III
Grant R. Gerhart; Chuck M. Shoemaker, Editor(s)

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