Principled Modeling and Automatic Classification for Enhancing the Reusability of Problem Solving Methods of Expert Systems

  • Authors:

  • John Yen;Swee Hor Teh;William M. Lively

  • Affiliations:

  • Department of Computer Science, Texas A&M University, College Station, TX 77843-3112. E-mail: yen@cs.tamu.edu;Applied Materials, 2695 Augustine Dr., M/S 0978, Santa Clara, CA 95054. E-mail: Swee-Hor_Teh@amat.com;Department of Computer Science, Texas A&M University, College Station, TX 77843-3112. E-mail: lively@cs.tamu.edu

  • Venue:
  • Applied Intelligence
  • Year:
  • 1998

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Abstract

Software reuse is widely believed to be a key to improvingsoftware productivity and quality in conventional software. Inexpert systems, much of the knowledge has been compiled (i.e.,compressed and restricted into effective procedures) and thismakes reusability difficult. One of the issues in modelingexpert systems for enhanced reusability is capturing explicity theunderlying problem solving designs. Principled knowledgerepresentation schemes have been used to model components of complexsoftware systems. However, the potential for applying theseprincipled modeling techniques for explicitly capturing the problemsolving designs of expert systems has not been fully explored. To overcomethis omission, we use an Artificial Intelligence knowledge representationscheme for developing an ontology of the software components to facilitatetheir classification and retrieval. The application of our ontologicalapproach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the applicationof principled domain modeling using two real world applications ofknowledge-based systems.