The paper proposes a structural Bayesian network learning in a biological decision-theoretic intelligent agent model to solve a herding problem. The proposed structural learning methods show that an agent can update its world model by changing the structure of its Bayesian network with the data gathered by experience. The structural learning of the Bayesian network is accomplished by implementing a score based greedy search algorithm. The search algorithm is designed heuristically and exhaustively. A complexity analysis for the search algorithms is performed. Intelligent agent software, IntelliAgent, is written to simulate the herding problem with one sheep and one dog.

Date of creation, presentation, or exhibit



Proceeding of the SMC 2001, IEEE International Conference on Systems, Man, and Cybernetics, pp. 1606 – 1611, October 7-10, 2001, Arizona. Copyright 2002 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. ISBN: 0-7803-7087-2Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Proceeding

Department, Program, or Center

Microelectronic Engineering (KGCOE)


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