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A Computational Study of Distributed Rule Learning

Author

Listed:
  • Riyaz Sikora

    (Industrial and Manufacturing Systems Engineering, The University of Michigan-Dearborn, 4901 Evergreen Road, Dearborn, Michigan 48128)

  • Michael J. Shaw

    (Beckman Institute for Advanced Science and Technology, The University of Illinois at Urbana-Champaign, 405 N. Mathews Avenue, Urbana, Illinois 61801)

Abstract

This report is concerned with a rule learning system called the Distributed Learning System (DLS). Its objective is two-fold: First, as the main contribution, the DLS as a rule-learning technique is described and the resulting computational performance is presented, with definitive computational benefits clearly demonstrated to show the efficacy of using the DLS. Second, the important parameters of the DLS are identified to show the characteristics of the Group Problem Solving (GPS) strategy as implemented in the DLS. On one hand this helps us pinpoint the critical designs of the DLS for effective rule learning; on the other hand this analysis can provide insight into the use of GPS as a more general rule-learning strategy.

Suggested Citation

  • Riyaz Sikora & Michael J. Shaw, 1996. "A Computational Study of Distributed Rule Learning," Information Systems Research, INFORMS, vol. 7(2), pages 189-197, June.
  • Handle: RePEc:inm:orisre:v:7:y:1996:i:2:p:189-197
    DOI: 10.1287/isre.7.2.189
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    Citations

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    Cited by:

    1. Rees, Jackie & Koehler, Gary J., 2006. "Learning genetic algorithm parameters using hidden Markov models," European Journal of Operational Research, Elsevier, vol. 175(2), pages 806-820, December.
    2. Rees, Jackie & Barkhi, Reza, 2001. "The problem of highly constrained tasks in group decision support systems," European Journal of Operational Research, Elsevier, vol. 135(1), pages 220-229, November.
    3. Riyaz Sikora & Michael J. Shaw, 1998. "A Multi-Agent Framework for the Coordination and Integration of Information Systems," Management Science, INFORMS, vol. 44(11-Part-2), pages 65-78, November.
    4. Amy Greenwald & Karthik Kannan & Ramayya Krishnan, 2010. "On Evaluating Information Revelation Policies in Procurement Auctions: A Markov Decision Process Approach," Information Systems Research, INFORMS, vol. 21(1), pages 15-36, March.

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