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Dynamic Flexible Constraint Satisfaction and its Application to AI Planning / Edition 1

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Constraint satisfaction is a fundamental technique for knowledge representation and inference in Artificial Intelligence. This success is founded on simplicity and generality: a constraint simply expresses a set of admissible value combinations among a number of variables. However, the classical formulation of a static constraint satisfaction problem (CSP) with inflexible constraints, all of which a solution must satisfy, is insufficient to model many real problems. This book demonstrates how a range of instances of these two powerful extensions can be combined in order to solve more complex problems.
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Product Details

  • ISBN-13: 9781852337643
  • Publisher: Springer London
  • Publication date: 11/14/2003
  • Series: Distinguished Dissertations Series
  • Edition description: 2004
  • Edition number: 1
  • Pages: 318
  • Product dimensions: 0.81 (w) x 6.14 (h) x 9.21 (d)

Table of Contents

List of Figures
1 Introduction 1
2 The Constraint Satisfaction Problem 13
3 Dynamic Flexible Constraint Satisfaction 65
4 An Empirical Study of Fuzzy rrDFCSPs 91
5 Dynamic CSP in Domain-independent AI Planning 159
6 GP-rrDCSP: Experimental Results 185
7 Flexible Planning Problems & Flexible Graphplan 203
8 FGP: Experimental Results 225
9 Conclusion 239
References 251
A Pseudo-code 263
B Proofs 271
C An Empirical Study of Fuzzy rrDFCSPs: Complete Results 279
D Planning Problems 281
Index 315
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