DKAS: A Distributed Knowledge Acquisition System in a DSS

Abstract:

Knowledge acquisition is the process of accumulating new information and relating it to what is already known. Knowledge acquisition has been regarded as the bottleneck in knowledge-based systems development. In this paper, a distributed knowledge acquisition system (DKAS) is introduced for automating decision rules construction from a set of examples in a decision support system. DKAS has the potential to include various learning mechanisms and employs a multi-agent and parallel processing paradigm. The implementation of a DKAS integrates inductive and deductive learning methods that use different learning strategies. A stock selection problem is used to demonstrate the effectiveness of DKAS in solving classification type problems. The performance of the DKAS in portfolio management is compared to the performance of the NYSE and the S&P 500. The results indicate that the rules derived from using the DKAS outperform both the NYSE and the S&P 500.

Key Words and Phrases::

• decision support systems
• deductive learning
• distributed knowledge acquisition system
• inductive learning
• portfolio management

Additional information

Notes on contributors

Melody Yihwa Kiang

Melody Yihwa Kiang is an Assistant Professor of Decision and Information Systems at Arizona State University. She received her M.S. from the University of Wisconsin, Madsion, and her Ph.D. in management science and information systems from the University of Texas, Austin. Her research emphasizes the development and applications of artificial intelligence techniques to a variety of management problems. Dr. Kiang has published papers in Management Science, The Journal of the Operational Research Society, Applications in Management Science, Journal of Computer Science in Economics and Management, and other journals. She is currently an associate editor of Decision Support Systems and is one of the guest editors of a special issue of that journal on qualitative reasoning in business, finance, and economics. She is a member of TIMS and IEEE.

Robert T. Chi

Robert T. Chi is an Assistant Professor of Information Systems at California State University at Long Beach. He received his M.S. from the University of Wisconsin, Madison, and his Ph.D. in management science and information systems from the University of Texas, Austin. His research interests include artificial intelligence (AI) application in management and finance, distributed AI, decision support systems, and executive information systems. Dr. Chi has published in Journal of Expert Systems with Applications. The Journal of the Operational Research Society. Annuals of Operational Research. International Journal of I ntelligent Systems. Journal of Knowledge Based Systems, and other professional journals. He is a member of TIMS and DSI.

Kar Yan Tam

Kar Yan Tam received a B.S. in mathematics and computer science from the University of Illinois, Urbana, an M.S. in computer science andaPh.D. in management information systems from Purdue University. He is currently a faculty member and a Weilun Fellow at the Hong Kong University of Science and Technology. Before joining HKUST he was Assistant Professor of Information Systems at the University of Texas, Austin. He has also held a Research Scientist position at Electronic Data Systems (EDS) where he conducted research in software engineering methodologies and tools. His research interests include AI applications in fmance and manufacturing, decision support systems, and information systems development. He has published in Management Science, Information Systems Research, Decision Support Systems, Information and Management, European Journal of Operational Research, The Journal of the Operational Research Society, International Journal of Production Research, Journal of Manufacturing Systems, Omega, Financial Management, and other professional journals. Dr. Tam has served on the program committees of many professional conferences and workshops. He is currently on the editorial board of Decision Support Systems and is the guest editor of a special issue of that journal on neural networks. He is a member of TIMS and IEEE.