Overview

Vague concepts are intrinsic to human communication. Somehow it would seems that vagueness is central to the flexibility and robustness of natural l- guage descriptions. If we were to insist on precise concept definitions then we would be able to assert very little with any degree of confidence. In many cases our perceptions simply do not provide sufficient information to allow us to verify that a set of formal conditions are met. Our decision to describe an individual as 'tall' is not generally based on any kind of accurate measurement of their height. Indeed it is part of the power of human concepts that they do not require us to make such fine judgements. They 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 int- ligent computer systems. This 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. I first became interested in these issues while working with Jim Baldwin to develop a theory of the probability of fuzzy events based on mass assi- ments.

Full Product Details

Author:   Jonathan Lawry
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2006 ed.
Volume:   12
Dimensions:   Width: 15.50cm , Height: 1.70cm , Length: 23.50cm
Weight:   0.571kg
ISBN:  

9780387290560

ISBN 10:   0387290567
Pages:   246
Publication Date:   11 January 2006
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Awaiting stock  
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Table of Contents

List 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: Multi-Dimensional And Multi-Instance Label Semantics 4.1 Descriptions Based on Many Attributes 4.2 Multi-dimensional Label Expressions and A-Sets 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: Non-Additive 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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