IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v7y1996i2p189-197.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.7.2.189
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.7.2.189?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orisre:v:7:y:1996:i:2:p:189-197. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.