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.

Contenuti:

Interchangeability and Solution Adaptation in Crisp CSPs.- Interchangeability in Soft CSPs.- Multi Agent Computation of Interchangeability in Distributed CSPs.- Interchangeability in Dynamic Environments.- Generic Case Adaptation Framework.- Conclusions.

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