Distributed Constraint Satisfaction


Book Description

Distributed Constraint Satisfaction gives an overview of Constraint Satisfaction Problems (CSPs), adapts related search algorithms and consistency algorithms for applications to multi-agent systems, and consolidates recent research devoted to cooperation in such systems. The techniques introduced are applied to various problems in multi-agent systems. Among the new approaches is a hybrid-type algorithm for weak-commitment search combining backtracking and iterative improvement. Also, an extension of the basic CSP formalization called "Partial CSP" is introduced in order to handle over-constrained CSPs.




Constraint Satisfaction Problems


Book Description

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.




Distributed Constraint Problem Solving and Reasoning in Multi-agent Systems


Book Description

Distributed and multi-agent systems are becoming more and more the focus of attention in artificial intelligence research and have already found their way into many practical applications. An important prerequisite for their success is an ability to flexibly adapt their behavior via intelligent cooperation. Successful reasoning about and within a multiagent system is therefore paramount to achieve intelligent behavior. Distributed Constraint Satisfaction Problems (DCSPs) and Distributed Constraint Optimization (minimization) Problems (DCOPs) are perhaps ubiquitous in distributed systems in dynamic environments. Many important problems in distributed environments and systems, such as action coordination, task scheduling and resource allocation, can be formulated and solved as DCSPs and DCOPs. Therefore, techniques for solving DCSPs and DCOPs as well as strategies for automated reasoning in distributed systems are indispensable tools in the research areas of distributed and multi-agent systems. They also provide promising frameworks to deal with the increasingly diverse range of distributed real world problems emerging from the fast evolution of communication technologies.The volume is divided in two parts. One part contains papers on distributed constraint problems in multi-agent systems. The other part presents papers on Agents and Automated Reasoning.




Constraint Processing


Book Description

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.




Handbook of Constraint Programming


Book Description

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.- Covers the whole field of constraint programming- Survey-style chapters- Five chapters on applications




Algorithms and Ordering Heuristics for Distributed Constraint Satisfaction Problems


Book Description

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. The distributed constraint satisfaction problem has such properties. Contents Introduction Part 1. Background on Centralized and Distributed Constraint Reasoning 1. Constraint Satisfaction Problems 2. Distributed Constraint Satisfaction Problems Part 2. Synchronous Search Algorithms for DisCSPs 3. Nogood Based Asynchronous Forward Checking (AFC-ng) 4. Asynchronous Forward Checking Tree (AFC-tree) 5. Maintaining Arc Consistency Asynchronously in Synchronous Distributed Search Part 3. Asynchronous Search Algorithms and Ordering Heuristics for DisCSPs 6. Corrigendum to “Min-domain Retroactive Ordering for Asynchronous Backtracking” 7. Agile Asynchronous BackTracking (Agile-ABT) Part 4. DisChoco 2.0: A Platform for Distributed Constraint Reasoning 8. DisChoco 2.0 9. Conclusion About the Authors Mohamed Wahbi is currently an associate lecturer at Ecole des Mines de Nantes in France. He received his PhD degree in Computer Science from University Montpellier 2, France and Mohammed V University-Agdal, Morocco in 2012 and his research focused on Distributed Constraint Reasoning.




Artificial Intelligence


Book Description

Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.




Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers


Book Description

Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.




Principles and Practice of Constraint Programming - CP 2004


Book Description

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.




Principles and Practice of Constraint Programming


Book Description

This book constitutes the proceedings of the 24th International Conference on Principles and Practice of Constraint Programming, CP 2018, held in Lille, France, in August 2018.The 41 full and 9 short papers presented in this volume were carefully reviewed and selected from 114 submissions. They deal with all aspects of computing with constraints including theory, algorithms, environments, languages, models, systems, and applications such as decision making, resource allocation, scheduling, configuration, and planning. The papers were organized according to the following topics/tracks: main technical track; applications track; CP and data science; CP and music; CP and operations research; CP, optimization and power system management; multiagent and parallel CP; and testing and verification.