Abstract - To study, model and enumerate intelligence, one must solve or approximate solutions to hard problems. Many methods have been advanced in this pursuit including simulated annealing [2], genetic algorithms [3], and evolutionary algorithms [4]. The Bayesian network method studied and reported here is another such method for optimizing a function, which we call utility. We use the Bayesian network method asit is analogous to the way people perceive, categorize, and calculate on their environment [5]. The Bayesian network method is a reasonable approximation to human reasoning process [5]. The Decision-theoretic approach is used specifically to solve the `Herding Problem' but the method and software is extensible to any other hard, discreet, optimization problem. We have successfully created a simulation environment in which we will be able to explore cooperation among multiple, intelligent, learning-capable agents.

Date of creation, presentation, or exhibit



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: 0-7803-7437-1Note: 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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