Handbook of Defeasible Reasoning and Uncertainty Management Systems

Algorithms for Uncertainty and Defeasible Reasoning

  • Jürg Kohlas
  • Serafín Moral

Table of contents

  1. Front Matter
    Pages i-v
  2. Volume Introduction

    1. Jürg Kohlas, Serafín Moral
      Pages 1-4
  3. Fundamentals

    1. Jürg Kohlas, Prakash P. Shenoy
      Pages 5-39
    2. Pierre Marquis
      Pages 41-145
  4. Algorithms for Logical Formalisms

    1. Salem Benferhat
      Pages 147-177
    2. R. Haenni, J. Kohlas, N. Lehmann
      Pages 221-288
  5. Algorithms for Numerical Formalisms

    1. Finn V. Jensen, Steffen L. Lauritzen
      Pages 289-320
    2. Pierre Hansen, Brigitte Jaumard
      Pages 321-367
    3. Andrés Cano, Serafín Moral
      Pages 369-420
    4. Nic Wilson
      Pages 421-475
    5. Hong Xu
      Pages 477-510
  6. Back Matter
    Pages 511-517

About this book


Reasoning under uncertainty is always based on a specified language or for­ malism, including its particular syntax and semantics, but also on its associated inference mechanism. In the present volume of the handbook the last aspect, the algorithmic aspects of uncertainty calculi are presented. Theory has suffi­ ciently advanced to unfold some generally applicable fundamental structures and methods. On the other hand, particular features of specific formalisms and ap­ proaches to uncertainty of course still influence strongly the computational meth­ ods to be used. Both general as well as specific methods are included in this volume. Broadly speaking, symbolic or logical approaches to uncertainty and nu­ merical approaches are often distinguished. Although this distinction is somewhat misleading, it is used as a means to structure the present volume. This is even to some degree reflected in the two first chapters, which treat fundamental, general methods of computation in systems designed to represent uncertainty. It has been noted early by Shenoy and Shafer, that computations in different domains have an underlying common structure. Essentially pieces of knowledge or information are to be combined together and then focused on some particular question or domain. This can be captured in an algebraic structure called valuation algebra which is described in the first chapter. Here the basic operations of combination and focus­ ing (marginalization) of knowledge and information is modeled abstractly subject to simple axioms.


algorithms complexity probabilistic network uncertainty

Editors and affiliations

  • Jürg Kohlas
    • 1
  • Serafín Moral
    • 2
  1. 1.University of FribourgSwitzerland
  2. 2.Universidad de GranadaSpain

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media B.V. 2000
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-481-5603-0
  • Online ISBN 978-94-017-1737-3
  • Buy this book on publisher's site