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Coevolution is a natural approach to evolve teams of agents which must cooperate to achieve some system objective. However, in many coevolutionary approaches, credit assignment is often subjective and context dependent, as the fitness of an individual agent strongly depends on the actions of the agents with which it collaborates. In order to alleviate this problem, we introduce a cooperative coevolutionary algorithm which biases the evolutionary search as well as shapes agent fitness functions to reward behavior that benefits the system. More specifically, we bias the search using a hall of fame approximation of optimal collaborators, and we shape the agent fitness using the difference evaluation function. Our results show that shaping agent fitness with the difference evaluation improves system performance by up to 50\%, and adding an additional fitness bias can improve performance by up to 75\%.
@inproceedings{tumer-colby_aamas12,
author = {M. Colby and K. Tumer},
title = {Shaping Fitness Functions for Coevolving Cooperative Multiagent Systems},
booktitle = {Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems},
month = {June},
pages ={425-432},
address = {Valencia, Spain},
abstract={Coevolution is a natural approach to evolve teams of agents which must cooperate to achieve some system objective. However, in many coevolutionary approaches, credit assignment is often subjective and context dependent, as the fitness of an individual agent strongly depends on the actions of the agents with which it collaborates. In order to alleviate this problem, we introduce a cooperative coevolutionary algorithm which biases the evolutionary search as well as shapes agent fitness functions to reward behavior that benefits the system. More specifically, we bias the search using a hall of fame approximation of optimal collaborators, and we shape the agent fitness using the difference evaluation function. Our results show that shaping agent fitness with the difference evaluation improves system performance by up to 50\%, and adding an additional fitness bias can improve performance by up to 75\%.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Multiagent Systems, Evolutionary Algorithms},
year = {2012}
}
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