Abstract

This paper details a learning decision-theoretic intelligent agent designed to solve the problem of guiding vehicles in the context of Personal Rapid Transit (PRT). The intelligent agents are designed using Bayesian Decision Networks. The agents are designed to utilize the known methods of machine learning with Bayesian Networks (BN): parameter learning and structure learning. In addition, a new method of machine learning with BNs, termed utility learning in this paper, is introduced. BN software for Matlab is used to realize the proposed agent. Additional software is written to simulate the PRT problem using various intelligent agents that utilize one or more learning methods.

Publication Date

2007

Comments

ISSN:1816-0948 Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Article

Department, Program, or Center

Microelectronic Engineering (KGCOE)

Campus

RIT – Main Campus

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