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Propositional, Probabilistic and Evidential Reasoning

Integrating Numerical and Symbolic Approaches

  • Weiru Liu

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 77)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Weiru Liu
    Pages 1-27
  3. Weiru Liu
    Pages 29-54
  4. Weiru Liu
    Pages 55-77
  5. Weiru Liu
    Pages 101-118
  6. Weiru Liu
    Pages 119-158
  7. Weiru Liu
    Pages 223-244
  8. Back Matter
    Pages 245-274

About this book

Introduction

The book systematically provides the reader with a broad range of systems/research work to date that address the importance of combining numerical and symbolic approaches to reasoning under uncertainty in complex applications. It covers techniques on how to extend propositional logic to a probabilistic one and compares such derived probabilistic logic with closely related mechanisms, namely evidence theory, assumption based truth maintenance systems and rough sets, in terms of representing and reasoning with knowledge and evidence.
The book is addressed primarily to researchers, practitioners, students and lecturers in the field of Artificial Intelligence, particularly in the areas of reasoning under uncertainty, logic, knowledge representation and reasoning, and non-monotonic reasoning.

Keywords

Symbol artificial intelligence intelligence knowledge representation nonmonotonic reasoning sets uncertainty

Authors and affiliations

  • Weiru Liu
    • 1
  1. 1.School of Information and Software EngineeringUniversity of Ulster at JordanstownNewtownabbey Co. AntrimNorthern Ireland UK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-7908-1811-6
  • Copyright Information Physica-Verlag Heidelberg 2001
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-7908-2493-3
  • Online ISBN 978-3-7908-1811-6
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site