The topic of this book is the following optimisation problem: given a set of discrete variables and a set of functions, each depending on a subset of the variables, minimise the sum of the functions over all variables. This fundamental research problem has been studied within several different contexts of discrete mathematics, computer science and artificial intelligence under different names: Min-Sum problems, MAP inference in Markov random fields (MRFs) and conditional random fields (CRFs), Gibbs energy minimisation, valued constraint satisfaction problems (VCSPs), and, for two-state variables, pseudo-Boolean optimisation. In this book the author presents general techniques for analysing the structure of such functions and the computational complexity of the minimisation problem, and he gives a comprehensive list of tractable cases. Moreover, he demonstrates that the so-called algebraic approach to VCSPs can be used not only for the search for tractable VCSPs, but also for other questions such as finding the boundaries to the applicability of certain algorithmic techniques. The book is suitable for researchers interested in methods and results from the area of constraint programming and discrete optimisation.

Introduces the universal-algebraic approach to classifying the computational complexity of constraint satisfaction problems.

Nowadays constraint satisfaction problems (CSPs) are ubiquitous in many different areas of computer science, from artificial intelligence and database systems to circuit design, network optimization, and theory of programming languages. Consequently, it is important to analyze and pinpoint the computational complexity of certain algorithmic tasks related to constraint satisfaction. The complexity-theoretic results of these tasks may have a direct impact on, for instance, the design and processing of database query languages, or strategies in data-mining, or the design and implementation of planners. This state-of-the-art survey contains the papers that were invited by the organizers after conclusion of an International Dagstuhl-Seminar on Complexity of Constraints, held in Dagstuhl Castle, Germany, in October 2006. A number of speakers were solicited to write surveys presenting the state of the art in their area of expertise. These contributions were peer-reviewed by experts in the field and revised before they were collated to the 9 papers of this volume. In addition, the volume contains a reprint of a survey by Kolaitis and Vardi on the logical approach to constraint satisfaction that first appeared in 'Finite Model Theory and its Applications', published by Springer in 2007.

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.

This seminal text of Computer Science, the most cited book on the subject, is now available for the first time in paperback. Constraint satisfaction is a decision problem that involves finite choices. It is ubiquitous. The goal is to find values for a set of variables that will satisfy a given set of constraints. It is the core of many applications in artificial intelligence, and has found its application in many areas, such as planning and scheduling. Because of its generality, most AI researchers should be able to benefit from having good knowledge of techniques in this field. Originally published in 1993, this now classic book was the first attempt to define the scope of constraint satisfaction. It covers both the theoretical and the implementation aspects of the subject. It provides a framework for studying this field, relates different research, and resolves ambiguity in a number of concepts and algorithms in the literature. This seminal text is arguably the most rigorous book in the field. All major concepts were defined in First Order Predicate Calculus. Concepts defined this way are precise and unambiguous.

Constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. In Constraint Processing, Rina Dechter synthesizes these contributions, as well as her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms.

The 10th International Conference on the Principles and Practice of Constraint Programming (CP 2003) was held in Toronto, Canada, during September 27 – October 1, 2004. Information about the conference can be found on the Web at http://ai.uwaterloo.ca/~cp2004/ Constraint programming (CP) is about problem modelling, problem solving, programming, optimization, software engineering, databases, visualization, user interfaces, and anything to do with satisfying complex constraints. It reaches into mathematics, operations research, arti?cial intelligence, algorithms, c- plexity, modelling and programming languages, and many aspects of computer science. Moreover, CP is never far from applications, and its successful use in industry and government goes hand in hand with the success of the CP research community. Constraintprogrammingcontinuesto beanexciting,?ourishingandgrowing research?eld,astheannualCPconferenceproceedingsamplywitness.Thisyear, from 158 submissions, we chose 46 to be published in full in the proceedings. Instead of selecting one overall best paper, we picked out four “distinguished” papers – though we were tempted to select at least 12 such papers. In addition we included 16 short papersin the proceedings– these were presentedas posters at CP 2004. This volume includes summaries of the four invited talks of CP 2004. Two speakers from industry were invited. However these were no ordinary industrial representatives,buttwoofthe leadingresearchersinthe CPcommunity:Helmut Simonis of Parc Technologies, until its recent takeover by Cisco Systems; and Jean Francoi ̧ s Puget, Director of Optimization Technology at ILOG. The other two invited speakers are also big movers and shakers in the researchcommunity.