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Learning team behaviors with adaptive heterogeneity

Published: 25 July 2005 Publication History

Abstract

The task of hard-coding agent behaviors to achieve desired team behaviors is very difficult, if not intractable. The complexity of multiagent problems can also rise exponentially with the number of agents and their behavioral sophistication. The field of cooperative multiagent learning promises solutions to these issues by employing automatic search methods to discover agent behaviors, and as such it has been the focus of numerous studies in recent years.

References

[1]
J. C. Bongard. The legion system: A novel approach to evolving heterogeneity for collective problem solving. In R. Poli et al, editors, Genetic Programming: Proceedings of EuroGP-2000, volume 1802, pages 16--28, Edinburgh, 15--16 2000. Springer-Verlag.
[2]
A. Hara and T. Nagao. Emergence of cooperative behavior using ADG; Automatically Defined Groups. In Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECCO-99), pages 1038--1046, 1999.
[3]
S. Luke. Genetic programming produced competitive soccer soft-bot teams for RoboCup97. In J. R. Koza et al, editors, Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 214--222. Morgan Kaufmann, 1998.
[4]
L. Panait and S. Luke. Collaborative multi-agent learning: A survey. Technical Report GMU-CS-TR-2003-01, Department of Computer Science, George Mason University, 2003.
[5]
R. P. Wiegand. Analysis of Cooperative Coevolutionary Algorithms. PhD thesis, Department of Computer Science, George Mason University, 2003.

Cited By

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  • (2010)Theoretical convergence guarantees for cooperative coevolutionary algorithmsEvolutionary Computation10.1162/EVCO_a_0000418:4(581-615)Online publication date: 1-Dec-2010

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  1. Learning team behaviors with adaptive heterogeneity

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    cover image ACM Conferences
    AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
    July 2005
    1407 pages
    ISBN:1595930930
    DOI:10.1145/1082473
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 25 July 2005

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    Author Tags

    1. dynamic role allocation
    2. multiagent learning

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    • (2010)Theoretical convergence guarantees for cooperative coevolutionary algorithmsEvolutionary Computation10.1162/EVCO_a_0000418:4(581-615)Online publication date: 1-Dec-2010

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