Foundations of Reasoning under Uncertainty

1. Front Matter

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2. Linear Fuzzy Regression Using Trapezoidal Fuzzy Intervals

   • Amory Bisserier, Reda Boukezzoula, Sylvie Galichet

   Pages 1-22

3. Fundamentals for Design and Construction of a Fuzzy Random Forest

   • Piero P. Bonissone, José Manuel Cadenas, María del Carmen Garrido, R. Andrés Díaz-Valladares

   Pages 23-42

4. Mining Exception Rules

   • Miguel Delgado, María Dolores Ruiz, Daniel Sánchez

   Pages 43-63

5. Degrees of Truth, Ill-Known Sets and Contradiction

   • Didier Dubois

   Pages 65-83

6. Algorithms for Trapezoidal Approximations of Fuzzy Numbers Preserving the Expected Interval

   • Przemysław Grzegorzewski

   Pages 85-98

7. Possibilistic Similarity Measures

   • Ilyes Jenhani, Salem Benferhat, Zied Elouedi

   Pages 99-123

8. Efficient Thresholded Tabulation for Fuzzy Query Answering

   • Pascual Julián, Jesús Medina, Ginés Moreno, Manuel Ojeda-Aciego

   Pages 125-141

9. Linguistic Summaries of Time Series: On Some Additional Data Independent Quality Criteria

   • Janusz Kacprzyk, Anna Wilbik

   Pages 143-166

10. Preference Modelling Using the Level-Dependent Choquet Integral with Respect to Łukasiewicz Filters

    • Martin Kalina, Dana Hliněná, Pavol Král’

    Pages 167-188

11. Majority Rules Generated by Mixture Operators

    • Bonifacio Llamazares, Ricardo Alberto Marques Pereira

    Pages 189-201

12. Belief Function Correction Mechanisms

    • David Mercier, Thierry Denœux, Marie-Hélène Masson

    Pages 203-222

13. A Comparison of Conditional Coherence Concepts for Finite Spaces

    • Enrique Miranda

    Pages 223-245

14. On Evidential Markov Chains

    • Hélène Soubaras

    Pages 247-264

15. Back Matter

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Uncertainty exists almost everywhere, except in the most idealized situations; it is not only an inevitable and ubiquitous phenomenon, but also a fundamental sci- ti?c principle. Furthermore, uncertainty is an attribute of information and, usually, decision-relevant information is uncertain and/or imprecise, therefore the abilities to handle uncertain information and to reason from incomplete knowledge are c- cial features of intelligent behaviour in complex and dynamic environments. By carefully exploiting our tolerance for imprecision and approximation we can often achieve tractability, robustness, and better descriptions of reality than traditional - ductive methods would allow us to obtain. In conclusion, as we move further into the ageofmachineintelligence,theproblemofreasoningunderuncertainty,in other words, drawing conclusions from partial knowledge, has become a major research theme. Not surprisingly,the rigoroustreatment of uncertaintyrequiressophisticated - chinery, and the present volume is conceived as a contribution to a better und- standing of the foundations of information processing and decision-making in an environment of uncertainty, imprecision and partiality of truth. This volume draws on papers presented at the 2008 Conference on Information Processing and Management of Uncertainty (IPMU), held in Malaga, ´ Spain, or- nized by the University of Mal ´ aga. The conference brought together some of the world’s leading experts in the study of uncertainty.

• Management of Uncertainty
• Reasoning in Uncertainty
• fuzziness
• fuzzy
• modeling
• uncertainty

• LIP6, Université Pierre et Marie Curie, Paris, France

  Bernadette Bouchon-Meunier

• European Centre for Soft Computing, Edificio Científico-Tecnológico., Mieres, Spain

  Luis Magdalena

• Computer Science Faculty, University of Málaga, Málaga, Spain

  Manuel Ojeda-Aciego

• Computer Science Faculty, University of Granada, Granada, Spain

  José-Luis Verdegay

• Machine Intelligence Institute, Iona College, New Rochelle, USA

  Ronald R. Yager

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