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Information, Uncertainty and Fusion

  • Book
  • © 2000

Overview

Editors:
  1. Bernadette Bouchon-Meunier

    1. University of Paris, France

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    You can also search for this editor in PubMed   Google Scholar

  2. Ronald R. Yager

    1. Iona College, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Lotfi A. Zadeh

    1. University of California, Berkeley, USA

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 516)

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Table of contents (35 chapters)

  1. Front Matter

    Pages i-x
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  2. Information

    1. Front Matter

      Pages 1-1
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    2. Revision by Translation

      • Dov Gabbay, Odinaldo Rodrigues, Alessandra Russo
      Pages 3-31

    3. Ambiguous Implications in Knowledge-Based Systems Described by Entity-Characteristic Tables

      • Silviu Guiasu
      Pages 33-44

    4. Functional Dependencies and the Design of Relational Databases Involving Imprecise Data

      • Patrick Bosc, Ludovic Liétard
      Pages 45-56

    5. A Query Construct for Paraconsistent Databases

      • Rajiv Bagai
      Pages 57-67

    6. Fuzzy classificatory object recognition for crisis detection

      • Henrik L. Larsen, Ronald R. Yager
      Pages 69-80

    7. An approach to using degrees of belief in BDI agents

      • Simon Parsons, Paolo Giorgini
      Pages 81-92

    8. Representation of Comonotonically Additive Functional by Choquet Integral

      • Yasuo Narukawa, Toshiaki Murofushi, Michio Sugeno
      Pages 93-104

    9. On Lower and Upper Approximation of Fuzzy Measures by k-Order Additive Measures

      • Michel Grabisch
      Pages 105-118

    10. Generated Fuzzy Quantities and Their Orderings

      • Etienne E. Kerre, Milan MareÅ¡, Radko Mesiar
      Pages 119-129

    11. On neurofuzzy and fuzzy decision tree approaches

      • Cristina Olaru, Louis Wehenkel
      Pages 131-145

    12. A Fuzzy Extension to a Temporal Parsimonious Covering Theory

      • Sandra Sandri, Jacques Wainer
      Pages 147-158

  3. Uncertainty

    1. Front Matter

      Pages 159-159
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    2. Probabilities of Future Decisions

      • Dennis Nilsson, Finn V. Jensen
      Pages 161-171

    3. Representational Issues for Risk Scenarios

      • Sophia Langley
      Pages 173-184

    4. Zero probabilities in stochastic independence

      • G. Coletti, R. Scozzafava
      Pages 185-196

    5. Computing the Probability of Formulas Representing Events in Product Spaces

      • P. A. Monney, B. Anrig
      Pages 197-208

    6. Uncertainty Handling for Distributed Database Integration and Knowledge Discovery

      • S. I. McClean, B. W. Scotney
      Pages 209-220

    7. A Rule-based Language for Probabilistic Reasoning

      • S. K. M. Wong
      Pages 221-232
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Keywords

About this book

As we stand at the precipice of the twenty first century the ability to capture and transmit copious amounts of information is clearly a defining feature of the human race. In order to increase the value of this vast supply of information we must develop means for effectively processing it. Newly emerging disciplines such as Information Engineering and Soft Computing are being developed in order to provide the tools required. Conferences such as the International Conference on Information Processing and ManagementofUncertainty in Knowledge-based Systems (IPMU) are being held to provide forums in which researchers can discuss the latest developments. The recent IPMU conference held at La Sorbonne in Paris brought together some of the world's leading experts in uncertainty and information fusion. In this volume we have included a selection ofpapers from this conference. What should be clear from looking at this volume is the number of different ways that are available for representing uncertain information. This variety in representational frameworks is a manifestation of the different types of uncertainty that appear in the information available to the users. Perhaps, the representation with the longest history is probability theory. This representation is best at addressing the uncertainty associated with the occurrence of different values for similar variables. This uncertainty is often described as randomness. Rough sets can be seen as a type of uncertainty that can deal effectively with lack of specificity, it is a powerful tool for manipulating granular information.

Editors and Affiliations

  • University of Paris, France

    Bernadette Bouchon-Meunier

  • Iona College, USA

    Ronald R. Yager

  • University of California, Berkeley, USA

    Lotfi A. Zadeh

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