Computational Models for Diagnostic Problem Solving

  • Yun Peng
  • James A. Reggia
Chapter
Part of the Symbolic Computation book series (SYMBOLIC)

Abstract

We now turn to the issue of automating diagnostic problem-solving and briefly survey representative previous work in this area. Such work is substantial, going back to almost the advent of electronic stored-program computers [Reggia85f], and for this reason the material that follows must unfortunately be quite selective. It is organized into three sections. The first section describes some basic concepts of knowledge-based systems. Two important methods that have been used widely to implement knowledge-based diagnostic systems, statistical pattern classification and rule-based deduction, are briefly described. The second section describes another class of systems which we will refer to as association-based abductive systems. These latter models capture the spirit of abductive reasoning in computer models. Two substantial examples of such systems are given and used to introduce the basic terminology of parsimonious covering theory in an informal, intuitive fashion. The third and the final section briefly addresses some practical issues that arise in implementing computational models for diagnosis.

Keywords

Benzene Petroleum Migraine Radium Sulfuric Acid 

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Copyright information

© Springer Science+Business Media New York 1990

Authors and Affiliations

  • Yun Peng
    • 1
    • 2
  • James A. Reggia
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA
  2. 2.The Institute of SoftwareAcademia SinicaBeijingChina

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