Distributed Constraint Problem Solving And Reasoning In Multi Agent Systems
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Distributed Constraint Problem Solving and Reasoning in Multi-agent Systems
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 Satisfaction Techniques for Agent-Based Reasoning
Author: Nicoleta Neagu
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-03-30
Constraint satisfaction problems are significant in the domain of automated reasoning for artificial intelligence. They can be applied to the modeling and solving of a wide range of combinatorial applications such as planning, scheduling and resource sharing in a variety of practical domains such as transportation, production, supply-chains, network management and human resource management. In this book we study new techniques for solving constraint satisfaction problems, with a special focus on solution adaptation applied to agent reasoning.
Handbook of Constraint Programming
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