An important aspect of multi agent systems are agent reasoning techniques for problem solving, either at the level of a single agent or at the level of distributed collaboration amongst multiple agents. Constraint satisfaction problems are significant in the domain of automated reasoning for artificial intelligence. They can be applied to modeling and solving of a wide range of combinatorial applications such as planning, scheduling and resource sharing in a variety of practical domains e.g. 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. Most work in constraint satisfaction has focused on computing a solution to a given problem. In practice, it often occurs that an existing solution needs to be modified to satisfy additional criteria or accommodate changes in the problem. Based on constraint satisfaction problem structures and their symmetries, we develop techniques for adapting solutions in applications and show how these techniques can be used when the agent is situated in dynamic and distributed environments.
This book is addressed to researchers in the artificial intelligence domain who are interested in constraint satisfaction techniques for agent reasoning. Moreover, as these methods are important for many applications such as planning, scheduling, diagnosis and resource allocation, researchers and application engineers in these domains will also benefit from applying the techniques described in this book.