Introduction: Special Issue on Distributed Constraint Satisfaction

Constraint satisfaction is one of the most successful problem solving paradigms in Artificial Intelligence. Since its original development over 30 years ago [2–4], it has found numerous applications in almost all areas of AI. Its most common applications today are in configuration, planning, scheduling and resource allocation, and form the basis of a significant software industry. With increasing use of the Internet, many of these applications now pose themselves in a multi-agent setting where variables and/or constraints of the problem are controlled by different agents. Distributed constraint satisfaction addresses this setting. If all knowledge about the problem can be gathered into one agent, this agent could solve the problem alone using traditional centralized constraint satisfaction algorithms. However, such a centralized solution is often inadequate or even impossible. Here are some reasons why distributed methods may be desirable: