IDEAS home Printed from https://ideas.repec.org/h/spr/ihichp/978-3-540-48713-5_30.html
   My bibliography  Save this book chapter

Adaptive Decision Support Systems via Problem Processor Learning

In: Handbook on Decision Support Systems 1

Author

Listed:
  • Clyde Holsapple

    (University of Kentucky)

  • Varghese S. Jacob

    (University of Texas)

  • Ramakrishnan Pakath

    (University of Kentucky)

  • Jigish S. Zaveri

    (Morgan State University)

Abstract

In this chapter, we describe the potential advantages of developing adaptive decision support systems (adaptive DSSs) for the efficient and/or effective solution of problems in complex domains. The problem processing components of DSSs that subscribe to existing DSS paradigms typically utilize supervised learning strategies to acquire problem processing knowledge (PPK). On the other hand, the problem processor of an adaptive DSS utilizes unsupervised inductive learning, perhaps in addition to other forms of learning, to acquire some of the necessary PPK. Thus, adaptive DSSs are, to some extent, self-teaching systems with less reliance on external agents for PPK acquisition. To illustrate these notions, we examine an application in the domain concerned with the scheduling of jobs in flexible manufacturing systems (FMSs). We provide an architectural description for an adaptive DSS for supporting static scheduling decisions in FMSs and illustrate key problem processing features of the system using an example.

Suggested Citation

  • Clyde Holsapple & Varghese S. Jacob & Ramakrishnan Pakath & Jigish S. Zaveri, 2008. "Adaptive Decision Support Systems via Problem Processor Learning," International Handbooks on Information Systems, in: Handbook on Decision Support Systems 1, chapter 30, pages 659-696, Springer.
    • Handle: RePEc:spr:ihichp:978-3-540-48713-5_30
    DOI: 10.1007/978-3-540-48713-5_30
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:ihichp:978-3-540-48713-5_30. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.