Constraint Satisfaction Problems
CSP Formalisms and Techniques
|Published:||Jan 18 2013|
|Posted:||Jul 31 2016|
|Book size:||2.67 MB|
A Constraint Satisfaction Problem (CSP) consists of a set ofvariables, a domain of values for each variable and a set ofconstraints. The objective is to assign a value for each variablesuch that all constraints are satisfied. CSPs continue to receiveincreased attention because of both their high complexity and theiromnipresence in academic, industrial and even reallife problems.This is why they are the subject of intense research in bothartificial intelligence and operations research. This bookintroduces the classic CSP and details severalextensions/improvements of both formalisms and techniques in orderto tackle a large variety of problems. Consistency, flexible,dynamic, distributed and learning aspects are discussed andillustrated using simple examples such as the nqueen 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 TunisScience City in Tunisia, Professor at the University of Tunis, aswell as the founding president of the Tunisian Association ofArtificial Intelligence and the founding director of the SOIEresearch laboratory. His research areas include MAS, CSP, transportand production logistics, metaheuristics and security inM/Egovernment. He has led several national and internationalresearch projects, supervised 30 PhD theses and more than 50Master s theses, coauthored about 300 journal, conferenceand book research papers, written two text books on metaheuristicsand production logistics and coauthored three others.
DisCSP (Distributed Constraint Satisfaction Problem) is a general framework for solving distributed problems arising in Distributed Artificial Intelligence.A wide variety of problems in artificial intelligence are solved using the constraint satisfaction problem paradigm. However, there are several applications in multi-agent coordination that are of a distributed nature. In this type of application, the knowledge about the problem, that is, variables and constraints, may be logically or geographically distributed among physical distributed agents. This distribution is mainly due to privacy and/or security requirements. Therefore, a distributed model allowing a decentralized solving process is more adequate to model and solve such kinds of problem. T...
Programming languages are often classified according to their paradigms, e.g. imperative, functional, logic, constraint-based, object-oriented, or aspect-oriented. A paradigm characterizes the style, concepts, and methods of the language for describing situations and processes and for solving problems, and each paradigm serves best for programming in particular application areas. Real-world problems, however, are often best implemented by a combination of concepts from different paradigms, because they comprise aspects from several realms, and this combination is more comfortably realized using multiparadigm programming languages. This book deals with the theory and practice of multiparadigm constraint programming languages. The author first elaborat...
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