A major challenge in constraint programming is to develop efficient generic approaches to solve instances of the constraint satisfaction problem (CSP). With this important aim in mind, this book provides an accessible synthesis of the field, including direct access to the author’s research in this area, divided into four main topics: representation, inference, search and learning. The results obtained, and presented in this book, have a wide applicability, regardless of the nature of the problem to be solved or the type of constraints involved, making it an extremely user-friendly resource for those involved in this field.
1. Constraint Networks
2. Random and Structured Networks
Part 1. Inference
4. Generic GAC Algorithms
5. Generalized Arc Consistency for Table Constraints
6. Singleton Arc Consistency
7. Path and Dual Consistency
Part 2. Search
8. Backtrack Search
9. Guiding Search toward Conflicts
10. Restarts and Nogood Recording
11. State-based Reasoning
12. Symmetry Breaking
Christophe Lecoutre is Assistant Professor at the University of Artois, France.