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Uncertain Inference

Uncertain Inference

£44.99

  • Date Published: October 2001
  • availability: Available
  • format: Paperback
  • isbn: 9780521001014

£ 44.99
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  • Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. For example, it is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. Thisbook seeks to provide a clear exposition of these approaches within a unified framework. The principal market for the book will be students and professionals in philosophy, computer science, and AI. Among the special features of the book are a chapter on evidential probability, which has not received a basic exposition before; chapters on nonmonotonic reasoning and theory replacement, matters rarely addressed in standard philosophical texts; and chapters on Mill's methods and statistical inference that cover material sorely lacking in the usual treatments of AI and computer science.

    • Detailed coverage of different approaches to uncertainty with extensive examples
    • Will appeal to students in AI and computer science, as well as philosophy
    • Kyburg is well-known in this area and author of many books (including KYBURG/Theory and Measurement/1984/0521 248787)
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    Product details

    • Date Published: October 2001
    • format: Paperback
    • isbn: 9780521001014
    • length: 312 pages
    • dimensions: 255 x 179 x 19 mm
    • weight: 0.56kg
    • availability: Available
  • Table of Contents

    Preface
    1. Historical background
    2. First order logic
    3. The probability calculus
    4. Interpretations of probability
    5. Nonstandard measures of support
    6. Nonmonotonic reasoning
    7. Theory replacement
    8. Statistical inference
    9. Evidential probability
    10. Semantics
    11. Applications
    12. Scientific inference.

  • Authors

    Henry E. Kyburg, Jr, University of Rochester, New York

    Choh Man Teng, Institute for Human and Machine Intelligence

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