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Studies in Computational Intelligence #12: Modelling and Reasoning with Vague Concepts

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Synopses & Reviews

Publisher Comments:

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.

Synopsis:

This volume introduces a formal representation framework for modelling and reasoning, that allows us to quantify the uncertainty inherent in the use of vague descriptions to convey information between intelligent agents. This can then be applied across a range of applications areas in automated reasoning and learning. The utility of the framework is demonstrated by applying it to problems in data analysis where the aim is to infer effective and informative models expressed as logical rules and relations involving vague concept descriptions. The author also introduces a number of learning algorithms within the framework that can be used for both classification and prediction (regression) problems. It is shown how models of this kind can be fused with qualitative background knowledge such as that provided by domain experts. The proposed algorithms will be compared with existing learning methods on a range of benchmark databases such as those from the UCI repository.

Synopsis:

In many emerging information technologies there is a clear need for automated learning from data, usually collected in the form of large databases. In machine learning the fundamental goal is to infer robust models with good generalization and predictive accuracy. 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. The book gives a rigorous introduction to the theory, illustrated with many examples, and suggests clear operational semantics for 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.   

Table of Contents

Introduction.- Vague Concepts and Fuzzy Sets.- Label Semantics.- Multi-Dimensional and Multi-Instance Label Semantics.- Information from Vague Concepts.- Learning Linguistic Models from Data.- Fusing Knowledge and Data.- Non-Additive Appropriateness Measures.

Product Details

ISBN:
9780387290560
Author:
Lawry, Jonathan
Publisher:
Springer
Author:
Lawry, J.
Subject:
Computer Science
Subject:
Logic
Subject:
Artificial Intelligence
Subject:
Mathematical models
Subject:
Fuzzy logic
Subject:
Artificial Intelligence - General
Subject:
Intelligence (AI) & Semantics
Subject:
Computational linguistics
Subject:
Fuzzy sets
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Complexity
Subject:
Pattern recognition.
Subject:
Information and Communication, Circuits
Subject:
Probability and Statistics in Computer Science
Subject:
Mathematical Logic and Formal Languages
Subject:
Computers-Reference - General
Copyright:
Edition Number:
1
Edition Description:
Book
Series:
Studies in Computational Intelligence
Series Volume:
12
Publication Date:
January 2006
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
271
Dimensions:
235 x 155 mm

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Personal Computers » General
Engineering » Engineering » General Engineering
Reference » Bibliography and Library Science
Science and Mathematics » Mathematics » Foundations and Logic
Science and Mathematics » Mathematics » Logic and Philosophy

Studies in Computational Intelligence #12: Modelling and Reasoning with Vague Concepts New Hardcover
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$180.25 In Stock
Product details 271 pages Springer - English 9780387290560 Reviews:
"Synopsis" by , This volume introduces a formal representation framework for modelling and reasoning, that allows us to quantify the uncertainty inherent in the use of vague descriptions to convey information between intelligent agents. This can then be applied across a range of applications areas in automated reasoning and learning. The utility of the framework is demonstrated by applying it to problems in data analysis where the aim is to infer effective and informative models expressed as logical rules and relations involving vague concept descriptions. The author also introduces a number of learning algorithms within the framework that can be used for both classification and prediction (regression) problems. It is shown how models of this kind can be fused with qualitative background knowledge such as that provided by domain experts. The proposed algorithms will be compared with existing learning methods on a range of benchmark databases such as those from the UCI repository.
"Synopsis" by , In many emerging information technologies there is a clear need for automated learning from data, usually collected in the form of large databases. In machine learning the fundamental goal is to infer robust models with good generalization and predictive accuracy. 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. The book gives a rigorous introduction to the theory, illustrated with many examples, and suggests clear operational semantics for 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.   
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