Modelling and Reasoning with Vague Concepts

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Studies in Computational Intelligence 12

ISBN: 0387290567
ISBN 13: 9780387290560
Autor: Lawry, Jonathan
Verlag: Springer Verlag GmbH
Umfang: xxv, 246 S.
Erscheinungsdatum: 11.01.2006
Auflage: 1/2006
Produktform: Gebunden/Hardback
Einband: GEB

Vagueness is central to the flexibility and robustness of natural language descriptions. Vague concepts are robust to the imprecision of our perceptions, while still allowing us to convey useful, and sometimes vital, information. The study of vagueness in Artificial Intelligence (AI) is therefore motivated by the desire to incorporate this robustness and flexibility into intelligent computer systems. Such a goal, however, requires a formal model of vague concepts that will allow us to quantify and manipulate the uncertainty resulting from their use as a means of passing information between autonomous agents. This volume outlines a formal representation framework for modelling and reasoning with vague concepts in Artificial Intelligence. The new calculus has many applications, especially in automated reasoning, learning, data analysis and information fusion. This book gives a rigorous introduction to label semantics theory, illustrated with many examples, and suggests clear operational interpretations of the proposed measures. It also provides a detailed description of how the theory can be applied in data analysis and information fusion based on a range of benchmark problems.

Artikelnummer: 1447216 Kategorie:

Beschreibung

InhaltsangabeList of Figures Preface Acknowledgments Foreword 1: Introduction 2: Vague Concepts And Fuzzy Sets 2.1 Fuzzy Set Theory 2.2 Functionality and Truth-Functionality 2.3 Operational Semantics for Membership Functions 3: Label Semantics 3.1 Introduction and Motivation 3.2 Appropriateness Measures and Mass Assignments on Labels 3.3 Label Expressions and lambda-Sets 3.4 A Voting Model for Label Semantics 3.5 Properties of Appropriateness Measures 3.6 Functional Label Semantics 3.7 Relating Appropriateness Measures to Dempster-Shafer Theory 3.8 Mass Selection Functions based on t-norms 3.9 Alternative Mass Selection Functions 3.10 An Axiomatic Approach to Appropriateness Measures 3.11 Label Semantics as a Model of Assertions 3.12 Relating Label Semantics to Existing Theories of Vagueness 4: MultiDimensional And MultiInstance Label Semantics 4.1 Descriptions Based on Many Attributes 4.2 Multidimensional Label Expressions and ASets 4.3 Properties of Multi-dimensional Appropriateness Measures 4.4 Describing Multiple Objects 5: Information From Vague Concepts 5.1 Possibility Theory 5.2 The Probability of Fuzzy Sets 5.3 Bayesian Conditioning in Label Semantics 5.4 Possibilistic Conditioning in Label Semantics 5.5 Matching Concepts 5.6 Conditioning From Mass Assignments in Label Semantics 6: Learning Linguistic Models From Data 6.1 Defining Labels for Data Modelling 6.2 Bayesian Classification using Mass Relations 6.3 Prediction using Mass Relations 6.4 Qualitative Information from Mass Relations 6.5 Learning Linguistic Decision Trees 6.6 Prediction using Decision Trees 6.7 Query evaluation and Inference from Linguistic Decision Trees 7: Fusing Knowledge And Data 7.1 From Label Expressions to Informative Priors 7.2 Combining Label Expressions with Data 8: NonAdditive Appropriateness Measures 8.1 Properties of Generalised Appropriateness Measures 8.2 Possibilstic Appropriateness Measures 8.3 An Axiomatic Approach to Generalised Appropriateness Measures 8.4 The Law of Excluded Middle References Index

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