A Constraint Satisfaction Problem (CSP) consists of a set of variables, a domain of values for each variable and a set of constraints. The objective is to assign a value for each variable such that all constraints are satisfied. CSPs continue to receive increased attention because of both their high complexity and their omnipresence in academic, industrial and even real-life problems.
This is why they are the subject of intense research in both artificial intelligence and operations research.
This book introduces the classic CSP and details several extensions/improvements of both formalisms and techniques in order to tackle a large variety of problems. Consistency, flexible, dynamic, distributed and learning aspects are discussed and illustrated using simple examples such as the n-queen problem.
Contents:
1. Foundations of CSP.
2. Consistency Reinforcement Techniques.
3. CSP Solving Algorithms.
4. Search Heuristics.
5. Learning Techniques.
6. Maximal Constraint Satisfaction Problems.
7. Constraint Satisfaction and Optimization Problems.
8. Distibuted Constraint Satisfaction Problems.
Content:
Chapter 1 Foundations of CSP (pages 1โ28): Khaled Ghedira and Bernard Dubuisson
Chapter 2 Consistency Reinforcement Techniques (pages 29โ72): Khaled Ghedira and Bernard Dubuisson
Chapter 3 CSP Solving Algorithms (pages 73โ98): Khaled Ghedira and Bernard Dubuisson
Chapter 4 Search Heuristics (pages 99โ120): Khaled Ghedira and Bernard Dubuisson
Chapter 5 Learning Techniques (pages 121โ134): Khaled Ghedira and Bernard Dubuisson
Chapter 6 Maximal Constraint Satisfaction Problems (pages 135โ164): Khaled Ghedira and Bernard Dubuisson
Chapter 7 Constraint Satisfaction and Optimization Problems (pages 165โ180): Khaled Ghedira and Bernard Dubuisson
Chapter 8 Distributed Constraint Satisfaction Problems (pages 181โ220): Khaled Ghedira and Bernard Dubuisson