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Abductive Inference Models for Diagnostic Problem-Solving

  • Book
  • © 1990

Overview

Authors:
  1. Yun Peng

    1. Department of Computer Science, University of Maryland, College Park, USA
      The Institute of Software, Academia Sinica, Beijing, China

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  2. James A. Reggia

    1. Department of Computer Science, University of Maryland, College Park, USA

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: Symbolic Computation (SYMBOLIC)

Part of the book sub series: Artificial Intelligence (1064)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xii
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  2. Abduction and Diagnostic Inference

    • Yun Peng, James A. Reggia
    Pages 1-10

  3. Computational Models for Diagnostic Problem Solving

    • Yun Peng, James A. Reggia
    Pages 11-47

  4. Basics of Parsimonious Covering Theory

    • Yun Peng, James A. Reggia
    Pages 49-98

  5. Probabilistic Causal Model

    • Yun Peng, James A. Reggia
    Pages 99-147

  6. Diagnostic Strategies in the Probabilistic Causal Model

    • Yun Peng, James A. Reggia
    Pages 149-199

  7. Causal Chaining

    • Yun Peng, James A. Reggia
    Pages 201-226

  8. Parallel Processing for Diagnostic Problem-Solving

    • Yun Peng, James A. Reggia
    Pages 227-253

  9. Conclusion

    • Yun Peng, James A. Reggia
    Pages 255-268

  10. Back Matter

    Pages 269-285
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Keywords

About this book

Making a diagnosis when something goes wrong with a natural or m- made system can be difficult. In many fields, such as medicine or electr- ics, a long training period and apprenticeship are required to become a skilled diagnostician. During this time a novice diagnostician is asked to assimilate a large amount of knowledge about the class of systems to be diagnosed. In contrast, the novice is not really taught how to reason with this knowledge in arriving at a conclusion or a diagnosis, except perhaps implicitly through ease examples. This would seem to indicate that many of the essential aspects of diagnostic reasoning are a type of intuiti- based, common sense reasoning. More precisely, diagnostic reasoning can be classified as a type of inf- ence known as abductive reasoning or abduction. Abduction is defined to be a process of generating a plausible explanation for a given set of obs- vations or facts. Although mentioned in Aristotle's work, the study of f- mal aspects of abduction did not really start until about a century ago.

Authors and Affiliations

  • Department of Computer Science, University of Maryland, College Park, USA

    Yun Peng, James A. Reggia

  • The Institute of Software, Academia Sinica, Beijing, China

    Yun Peng

Bibliographic Information

  • Book Title: Abductive Inference Models for Diagnostic Problem-Solving

  • Authors: Yun Peng, James A. Reggia

  • Series Title: Symbolic Computation

  • DOI: https://doi.org/10.1007/978-1-4419-8682-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1990

  • Hardcover ISBN: 978-0-387-97343-2Published: 26 June 1990

  • Softcover ISBN: 978-1-4612-6450-7Published: 20 November 2012

  • eBook ISBN: 978-1-4419-8682-5Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: XII, 285

  • Topics: Artificial Intelligence

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