A HYBRID BAYESIAN NETWORK-BASED MULTI-AGENT SYSTEM AND A DISTRIBUTED SYSTEMS ARCHITECTURE FOR THE DRUG CRIME KNOWLEDGE MANAGEMENT

Parag C. Pendharkar () and Rahul Bhaskar ()
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Parag C. Pendharkar: School of Business Administration, Pennsylvania State University at Harrisburg, 777, W. Harrisburg Pike, Middletown, PA 17078, USA
Rahul Bhaskar: Information Systems, School of Business CSU-Fullerton, 800, California State University at Fullerton North College Blvd., Fullerton, CA 92831, USA

International Journal of Information Technology & Decision Making (IJITDM), 2003, vol. 02, issue 04, 557-576

Abstract: In this paper, we describe an approach for building a hybrid Bayesian network-based multi-agent system for drug crime knowledge management. We use distributed artificial intelligence architecture to create a multi-agent information system that integrates distributed knowledge sources and information to aid decision-making. Our comparison of the hybrid system with a previously developed stand-alone expert systemSherpa, which was in use at a large drug crime investigation facility, shows that the current system compares similar to the existing system in terms of efficiency and effectiveness of knowledge management. We illustrate how the proposed hybrid bayesian network-based can be implemented in the distributed computing network environment.

Keywords: Distributed artificial intelligence; multi-agent information systems; distributed problem solving; distributed computing (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1142/S0219622003000872

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