Fitness function shaping in multiagent cooperative coevolutionary algorithms

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• 作者：Mitchell Colby ; Kagan Tumer
• 关键词：Multiagent learning ; Cooperative coevolutionary algorithms ; Difference evaluation functions ; Fitness function shaping
• 刊名：Autonomous Agents and Multi-Agent Systems
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：31
• 期：2
• 页码：179-206
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Computer Systems Organization and Communication Networks; Computing Methodologies; Software Engineering/Programming and Operating Systems; User Interfaces and
• 出版者：Springer US
• ISSN：1573-7454
• 卷排序：31

文摘

Coevolution is a promising 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 promote behavior that benefits the system-level performance. More specifically, we bias the search using a hall of fame approximation of optimal collaborators, and 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 improves performance by up to 75 % in our experiments. Finally, an analysis of system performance as a function of computational cost demonstrates that this algorithm makes extremely efficient use of computational resources, having a higher performance as a function of computational cost than any other algorithm tested.