Learning from experience using a decision-theoretic intelligent agent in multi-agent systems

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Title: Learning from experience using a decision-theoretic intelligent agent in multi-agent systems
Author: Sahin, Ferat; Bay, John
Abstract: This paper proposes a decision-theoretic intelligent agent model to solve a herding problem and studies the learning from experience capabilities of the agent model. The proposed intelligent agent model is designed by combining Bayesian networks (BN) and influence diagrams (ID). The online Bayesian network learning method is proposed to accomplish the learning from experience. Intelligent agent software, IntelliAgent, is written to realize the proposed intelligent agent model and to simulate the agents in a problem domain. The same software is then used to simulate the herding problem with one sheep and one dog. Simulation results show that the proposed intelligent agent is successful in establishing a goal (herding) and learning other agents' behaviors.
Description: Copyright 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Mountain Workshop on Soft Computing in Industrial Applications Virginia Tech, Blacksburg, Virginia, June25-27,2001
Record URI: http://hdl.handle.net/1850/8930
Date: 2001-06

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