Soft Methods for Integrated Uncertainty Modelling

  • Jonathan Lawry
  • Enrique Miranda
  • Alberto Bugarin
  • Shoumei Li
  • Maria Angeles Gil
  • Przemys aw Grzegorzewski
  • Olgierd Hyrniewicz

Part of the Advances in Soft Computing book series (AINSC, volume 37)

Table of contents

  1. Front Matter
    Pages I-X
  2. Keynote Papers

  3. Soft Methods in Statistics and Random Information Systems

    1. Front Matter
      Pages I-X
    2. Olgierd Hryniewicz
      Pages 29-36
    3. Andreas Wünsche, Wolfgang Näther
      Pages 37-44
    4. Kevin Loquin, Olivier Strauss
      Pages 45-52
    5. Karel De Loof, Hans De Meyer, Bernard De Baets
      Pages 53-60
    6. Adel Mohammadpour, Ali Mohammad-Djafari
      Pages 61-69
    7. José Luis Aznarte M., José Manuel Benítez
      Pages 79-86
    8. González-Rodríguez G., Colubi A., D’Urso P., Giordani P.
      Pages 87-94
    9. Colubi A., González-Rodríguez G., Lubiano M.A., Montenegro M.
      Pages 95-102
    10. González-Rodríguez G., Colubi A., Gil M.A., Coppi R.
      Pages 103-110
    11. Edyta Mrówka, Przemysław Grzegorzewski
      Pages 111-118
  4. Probability of Imprecisely-Valued Random Elements with Applications

    1. Front Matter
      Pages I-X
    2. S.F. Cullender, W.-C. Kuo, C.C.A. Labuschagne, B.A. Watson
      Pages 121-128

About this book

Introduction

This edited volume is the proceedings of the 2006 International Conference on Soft Methods in Probability and Statistics (SMPS 2006) hosted by the Artificial Intelligence Group at the University of Bristol, between 5-7 September 2006. This is the third of a series of biennial conferences organized in 2002 by the Systems Research Institute from the Polish Academy of Sciences in Warsaw, and in 2004 by the Department of Statistics and Operational Research at the University of Oviedo in Spain. These conferences provide a forum for discussion and research into the fusion of soft methods with probability and statistics, with the ultimate goal of integrated uncertainty modelling in complex systems involving human factors. In addition to probabilistic factors such as measurement error and other random effects, the modelling process often requires us to make qualitative and subject judgments that cannot easily be translated into precise probability values. Such judgments give rise to a number of different types of uncertainty including; fuzziness if they are based on linguistic information; epistemic uncertainty when their reliability is in question; ignorance when they are insufficient to identify or restrict key modelling parameters; imprecision when parameters and probability distributions can only be estimated within certain bounds. Statistical theory has not traditionally been concerned with modelling uncertainty arising in this manner but soft methods, a range of powerful techniques developed within AI, attempt to address those problems where the encoding of subjective information is unavoidable. These are mathematically sound uncertainty modelling methodologies which are complementary to conventional statistics and probability theory. Therefore, a more realistic modelling process providing decision makers with an accurate reflection of the true current state of our knowledge (and ignorance) requires an integrated framework incorporating both probability theory, statistics and soft methods. This fusion motivates innovative research at the interface between computer science (AI), mathematics and systems engineering.

Keywords

artificial intelligence complex system complex systems computer-aided design (CAD) intelligence knowledge modeling modelling operations research statistics uncertainty

Editors and affiliations

  • Jonathan Lawry
    • 1
  • Enrique Miranda
    • 2
  • Alberto Bugarin
    • 3
  • Shoumei Li
    • 4
  • Maria Angeles Gil
    • 5
  • Przemys aw Grzegorzewski
    • 6
  • Olgierd Hyrniewicz
    • 7
  1. 1.AI Group Department of Engineering MathematicsUniversity of BristolBristolUK
  2. 2.Statistics and Operations ResearchRey Juan Carlos UniversitySpain
  3. 3.Intelligent Systems Group Department of Electronics & Computer ScienceUniversity of Santiago de Compostela Santiago de CompostelaSpain
  4. 4.Department of Applied MathematicsBeijing University of TechnologyBeijingP.R. China
  5. 5.Dpto. Estadistica e I.O y D.M. Calle Calvo Sotelo s/nUniversiad de Oviedo Fac. CienciasOviedoSpain
  6. 6.Systems Research Institute Polish Academy of SciencesWarsawPoland
  7. 7.Systems Research Institute Polish Academy of SciencesWarsawPoland

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-34777-1
  • Copyright Information Springer 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-34776-7
  • Online ISBN 978-3-540-34777-4
  • Series Print ISSN 1615-3871
  • About this book