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Hybrid Behavior Co-evolution and Structure Learning in Behavior-based Systems

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4 Author(s)
A. M. Farahmand ; Department of Electrical and Computer Engineering, University of Tehran, Iran. He is now with the Department of Computing Science, University of Alberta, Canada (e-mail: ; M. N. Ahmadabadi ; C. Lucas ; B. N. Araabi

Designing an intelligent situated agent is a difficult task as the designer must see the problem from the agent's standpoint considering all its sensors and actuators. We have devised a co-evolutionary/reinforcement learning hybrid method to automate the design of hierarchical behavior-based systems. In our approach, the design problem is decomposed into two separate parts: developing a repertoire of behaviors and organizing those behaviors in a structure. Mathematical formulation shows how to decompose the value of the structure to simpler components. These components can be estimated and used to find the optimal organization of behaviors during the agent's lifetime. Moreover, a novel co-evolutionary mechanism is suggested that evolves each type of behavior separately in their own genetic pool. Our method is applied to the decentralized multi-robot object lifting task which results in human-competitive performance.

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2006 IEEE International Conference on Evolutionary Computation

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