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Mitchell Colby

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

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AAMAS Conference 2016 Conference Paper

Local Approximation of Difference Evaluation Functions

Mitchell Colby
Theodore Duchow-Pressley
Jen Jen Chung
Kagan Tumer

Diﬀerence evaluation functions have resulted in excellent multiagent behavior in many domains, including air traf- ﬁc and mobile robot control. However, calculating diﬀerence evaluation functions requires determining the value of a counterfactual system objective function, which is often diﬃcult when the system objective function is unknown or global state and action information is unavailable. In this work, we demonstrate that a local estimate of the system evaluation function may be used to estimate diﬀerence evaluations using readily available information, allowing for difference evaluations to be computed in multiagent systems where the mathematical form of the objective function is not known. This approximation technique is tested in two domains, and we demonstrate that approximating diﬀerence evaluation functions results in better performance and faster learning than when using global evaluation functions. Finally, we demonstrate the eﬀectiveness of the learned policies on a set of Pioneer P3-DX robots.

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JAAMAS Journal 2015 Journal Article

Fitness function shaping in multiagent cooperative coevolutionary algorithms

Mitchell Colby
Kagan Tumer

Abstract 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.

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AAMAS Conference 2012 Conference Paper

Shaping Fitness Functions for Coevolving Cooperative Multiagent Systems

Mitchell Colby
Kagan Tumer

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 objective functions. Our results show that shaping agent fitness with the difference objective improves system performance by up to 50%, and adding an additional fitness bias can improve performance by up to 75%.

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