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Interval / Probabilistic Uncertainty and Non-Classical Logics

  • Editors
  • Van-Nam Huynh
  • Yoshiteru Nakamori
  • Hiroakira Ono
  • Jonathan Lawry
  • Vkladik Kreinovich
  • Hung T. Nguyen

Part of the Advances in Soft Computing book series (AINSC, volume 46)

Table of contents

  1. Front Matter
  2. Keynote Addresses

  3. Statistics under Interval Uncertainty and Imprecise Probability

  4. Uncertainty Modelling and Reasoning in Knowledge-Based Systems

    1. Front Matter
      Pages 85-85
    2. Elsa Carvalho, Jorge Cruz, Pedro Barahona
      Pages 115-128
  5. Rough Sets and Belief Functions

    1. Front Matter
      Pages 161-161
    2. Masahiro Inuiguchi, Yukihiro Yoshioka
      Pages 163-175
    3. Yaxin Bi, Xuhui Shen, Shengli Wu
      Pages 187-200
    4. Fabio Cuzzolin
      Pages 201-213
  6. Non-classical Logics

    1. Front Matter
      Pages 229-229
    2. Carol L. Walker, Elbert A. Walker
      Pages 245-255
    3. Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler, Jon Williamson
      Pages 268-279
    4. Jeff B. Paris, David Picado-Muiño, Michael Rosefield
      Pages 291-307
  7. Fuzziness and Uncertainty Analysis in Applications

  8. Back Matter

About this book

Introduction

Most successful applications of modern science and engineering, from discovering the human genome to predicting weather to controlling space missions, involve processing large amounts of data and large knowledge bases. The ability of computers to perform fast data and knowledge processing is based on the hardware support for super-fast elementary computer operations, such as performing arithmetic operations with (exactly known) numbers and performing logical operations with binary ("true"-"false") logical values. In practice, measurements are never 100% accurate. It is therefore necessary to find out how this input inaccuracy (uncertainty) affects the results of data processing. Sometimes, we know the corresponding probability distribution; sometimes, we only know the upper bounds on the measurement error -- which leads to interval bounds on the (unknown) actual value. Also, experts are usually not 100% certain about the statements included in the knowledge bases. A natural way to describe this uncertainty is to use non-classical logics (probabilistic, fuzzy, etc.).

This book contains proceedings of the first international workshop that brought together researchers working on interval and probabilistic uncertainty and on non-classical logics. We hope that this workshop will lead to a boost in the much-needed collaboration between the uncertainty analysis and non-classical logic communities, and thus, to better processing of uncertainty.

Keywords

Analysis fuzzy genome knowledge knowledge base knowledge-based system knowledge-based systems modeling modelling statistics uncertainty

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-77664-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-77663-5
  • Online ISBN 978-3-540-77664-2
  • Series Print ISSN 1615-3871
  • Series Online ISSN 1860-0794
  • Buy this book on publisher's site