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

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Abstract

Software reuse is widely believed to be a key to improving software productivity and quality in conventional software. In expert systems, much of the knowledge has been compiled (i.e., compressed and restricted into effective procedures) and this makes reusability difficult. One of the issues in modeling expert systems for enhanced reusability is capturing explicity the underlying problem solving designs. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques for explicitly capturing the problem solving designs of expert systems has not been fully explored. To overcome this omission, we use an Artificial Intelligence knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. The application of our ontological approach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the application of principled domain modeling using two real world applications of knowledge-based systems.

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Authors and Affiliations

  1. Department of Computer Science, Texas A&M University, College Station, TX 77843-3112

    John Yen & William M. Lively

  2. Applied Materials, 2695 Augustine Dr., M/S 0978, Santa Clara, CA, 95054

    Swee Hor Teh

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  1. John Yen
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  2. Swee Hor Teh
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  3. William M. Lively
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Yen, J., Teh, S.H. & Lively, W.M. Principled Modeling and Automatic Classification for Enhancing the Reusability of Problem Solving Methods of Expert Systems. Applied Intelligence 8, 139–155 (1998). https://doi.org/10.1023/A:1008296124032

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