Environmental robustness in multi-agent teams

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Environmental robustness in multi-agent teams

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents

Montreal, Québec, Canada

SESSION: Track 2: artificial life, evolutionary robotics, adaptive behavior, and evolvable hardware table of contents

Pages 177-184

Year of Publication: 2009

ISBN:978-1-60558-325-9

Authors

Terence Soule	University of Idaho, Moscow, ID, USA
Robert B. Heckendorn	University of Idaho, Moscow, ID, USA

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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ABSTRACT

Evolution has proven to be an effective method of training heterogeneous multi-agent teams of autonomous agents to explore unknown environments. Autonomous, heterogeneous agents are able to go places where humans are unable to go and perform tasks that would be otherwise dangerous or impossible to complete. However, a serious problem for practical applications of multi-agent teams is how to move from training environments to real-world environments. In particular, if the training environment cannot be made identical to the real-world environment how much will performance suffer? In this research we investigate how differences in training and testing environments affect performance. We find that while in general performance degrades from training to testing, for difficult training environments performance improves in the test environment. Further, we find distinct differences between the performance of different training algorithms with Orthogonal Evolution of Teams (OET) producing the best overall performance.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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