Constraint processing
Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning h...
Autor principal: | |
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Formato: | Libro electrónico |
Idioma: | Inglés |
Publicado: |
San Francisco :
Morgan Kaufmann Publishers
c2003.
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Edición: | 1st edition |
Colección: | Morgan Kaufmann Series in Artificial Intelligence
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Materias: | |
Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627117706719 |
Tabla de Contenidos:
- Front Cover; Constraint Processing; Copyright Page; Foreword; Contents; Preface; Chapter 1. Introduction; 1.1 Basic Concepts and Examples; 1.2 Overview by Chapter; 1.3 Mathematical Background; 1.4 Bibliographical Notes; 1.5 Exercises; Part I: Basics of Constraint Processing; Chapter 2. Constraint Networks; 2.1 Constraint Networks and Constraint Satisfaction; 2.2 Numeric and Boolean Constraints; 2.3 Properties of Binary Constraint Networks; 2.4 Summary; 2.5 Bibliographical Notes; 2.6 Exercises; Chapter 3. Consistency-Enforcing and Constraint Propagation; 3.1 Why Propagate Constraints?
- 3.2 Arc-Consistency3.3 Path-Consistency; 3.4 Higher Levels of i-Consistency; 3.5 Arc-Consistency for Nonbinary Constraints; 3.6 Constraint Propagation for Numeric and Boolean Constraints; 3.7 Trees, Bivalued Networks, and Horn Theories; 3.8 Summary; 3.9 Bibliographical Notes; 3.10 Exercises; Chapter 4. Directional Consistency; 4.1 Graph Concepts: Induced Width; 4.2 Directional Local Consistency; 4.3 Width versus Local Consistency; 4.4 Adaptive Consistency and Bucket Elimination; 4.5 Summary; 4.6 Bibliographical Notes; 4.7 Exercises; Chapter 5. General Search Strategies: Look-Ahead
- 5.1 The State Space Search5.2 Backtracking; 5.3 Look-Ahead Strategies; 5.4 Satisfiability: Look-Ahead in Backtracking; 5.5 Summary; 5.6 Bibliographical Notes; 5.7 Exercises; Chapter 6. General Search Strategies: Look-Back; 6.1 Conflict Sets; 6.2 Backjumping Styles; 6.3 Complexity of Backjumping; 6.4 Learning Algorithms; 6.5 Look-Back Techniques for Satisfiability; 6.6 Integration and Comparison of Algorithms; 6.7 Summary; 6.8 Bibliographical Notes; 6.9 Exercises; Chapter 7. Stochastic Greedy Local Search; 7.1 Greedy Local Search; 7.2 Random Walk Strategies
- 7.3 Hybrids of Local Search and Inference7.4 Summary; 7.5 Bibliographical Notes; 7.6 Exercises; Part II: Advanced Methods; Chapter 8. Advanced Consistency Methods; 8.1 Relational Consistency; 8.2 Directional Consistency Revisited; 8.3 Domain and Constraint Tightness; 8.4 Inference for Boolean Theories; 8.5 Row-Convex Constraints; 8.6 Linear Inequalities; 8.7 Summary; 8.8 Bibliographical Notes; 8.9 Exercises; Chapter 9. Tree Decomposition Methods; 9.1 Acyclic Networks; 9.2 Tree-Based Clustering; 9.3 ADAPTIVE-CONSISTENCY as Tree Decomposition; 9.4 Summary; 9.5 Bibliographical Notes
- 9.6 ExercisesChapter 10. Hybrids of Search and Inference: Time-Space Trade-Offs; 10.1 Specialized Cutset Schemes; 10.2 Hybrids: Conditioning First; 10.3 Hybrids: Inference First; 10.4 A Case Study of Combinatorial Circuits; 10.5 Summary; 10.6 Bibliographical Notes; 10.7 Exercises; Chapter 11. Tractable Constraint Languages; 11.1 The CSP Search Problem; 11.2 Constraint Languages; 11.3 Expressiveness of Constraint Languages; 11.4 Complexity of Constraint Languages; 11.5 Hybrid Tractability; 11.6 Summary; 11.7 Bibliographical Notes; 11.8 Exercises; Chapter 12. Temporal Constraint Networks
- 12.1 Qualitative Networks