A Hybrid Bayesian Network-Based Multi-Agent System And A Distributed Systems Architecture For The Drug Crime Knowledge Management

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 system Sherpa, 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.

[1]  Benjamin P.-C. Yen,et al.  Communication infrastructure in distributed scheduling , 2002 .

[2]  Chia-Hui Chang,et al.  Enabling Concept-Based Relevance Feedback for Information Retrieval on the WWW , 1999, IEEE Trans. Knowl. Data Eng..

[3]  Siddhartha Bhattacharyya,et al.  Inductive, Evolutionary, and Neural Computing Techniques for Discrimination: A Comparative Study* , 1998 .

[4]  Sati S. Sian,et al.  Extending Learning to Multiple Agents: Issues and a Model for Multi-Agent Machine Learning (MA-ML) , 1991, EWSL.

[5]  Kecheng Liu,et al.  A multi-agent decision support system for stock trading , 2002, IEEE Netw..

[6]  Michael J. Shaw,et al.  Learning and adaption in distributed artificial intelligence , 1990 .

[7]  Michael J. Shaw,et al.  A distributed problem-solving approach to inductive learning , 1990 .

[8]  Parag C. Pendharkar,et al.  The Wisconsin Division of Narcotics Enforcement Uses Multi-Agent Information Systems to Investigate Drug Crimes , 1999, Interfaces.

[9]  Gerhard Weiß,et al.  Adaptation and Learning in Multi-Agent Systems: Some Remarks and a Bibliography , 1995, Adaption and Learning in Multi-Agent Systems.

[10]  Robert R. Hoffman,et al.  The Problem of Extracting the Knowledge of Experts from the Perspective of Experimental Psychology , 1987, AI Mag..

[11]  Randall Davis,et al.  Frameworks for Cooperation in Distributed Problem Solving , 1988, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  Parag C. Pendharkar,et al.  Sherpa: a multi‐agent information system for the drug crime process and knowledge management , 1998 .

[13]  G. Peter Zhang,et al.  The Effect of Misclassification Costs on Neural Network Classifiers , 1999 .

[14]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[15]  Dustin Huntington,et al.  Web-based expert systems are on the way: Java-based Web delivery , 2000 .

[16]  Michael J. Shaw,et al.  Learning and Adaptation In Distributed Artificial Intelligence Systems , 1989, Distributed Artificial Intelligence.

[17]  Martha Grabowski,et al.  Knowledge Acquisition Methodologies: Survey and Empirical Assessment , 1988, ICIS.

[18]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[19]  Edmund H. Durfee,et al.  Evaluating Research in Cooperative Distributed Problem Solving , 1990, Distributed Artificial Intelligence.