Constraint Satisfaction Problems
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Constraint Satisfaction Problems
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. About the Authors Khaled Ghedira is the general managing director of the Tunis Science City in Tunisia, Professor at the University of Tunis, as well as the founding president of the Tunisian Association of Artificial Intelligence and the founding director of the SOIE research laboratory. His research areas include MAS, CSP, transport and production logistics, metaheuristics and security in M/E-government. He has led several national and international research projects, supervised 30 PhD theses and more than 50 Master’s theses, co-authored about 300 journal, conference and book research papers, written two text books on metaheuristics and production logistics and co-authored three others.
Complexity Classifications of Boolean Constraint Satisfaction Problems
Many fundamental combinatorial problems, arising in such diverse fields as artificial intelligence, logic, graph theory, and linear algebra, can be formulated as Boolean constraint satisfaction problems (CSP). This book is devoted to the study of the complexity of such problems. The authors' goal is to develop a framework for classifying the complexity of Boolean CSP in a uniform way. In doing so, they bring out common themes underlying many concepts and results in both algorithms and complexity theory. The results and techniques presented here show that Boolean CSP provide an excellent framework for discovering and formally validating "global" inferences about the nature of computation.
Foundations of Constraint Satisfaction
Foundations of Constraint Satisfaction discusses the foundations of constraint satisfaction and presents algorithms for solving constraint satisfaction problems (CSPs). Most of the algorithms described in this book are explained in pseudo code, and sometimes illustrated with Prolog codes (to illustrate how the algorithms could be implemented). Comprised of 10 chapters, this volume begins by defining the standard CSP and the important concepts around it and presenting examples and applications of CSPs. The reader is then introduced to the main features of CSPs and CSP solving techniques (problem reduction, searching, and solution synthesis); some of the most important concepts related to CSP solving; and problem reduction algorithms. Subsequent chapters deal with basic control strategies of searching which are relevant to CSP solving; the significance of ordering the variables, values and compatibility checking in searching; specialized search techniques which gain their efficiency by exploiting problem-specific features; and stochastic search approaches (including hill climbing and connectionist approaches) for CSP solving. The book also considers how solutions can be synthesized rather than searched for before concluding with an analysis of optimization in CSPs. This monograph can be used as a reference by artificial intelligence (AI) researchers or as a textbook by students on advanced AI courses, and should also help knowledge engineers apply existing techniques to solve CSPs or problems which embed CSPs.