Traditionally, constraint satisfaction has been applied in closedworld scenarios, where all choices and constraints are known from the beginning and fixed. With the Internet, many of the traditional CSP applications in resource allocation, scheduling and planning pose themselves in open-world settings, where choices and constraints are to be discovered from different servers in a network. We examine how such a distributed setting affects changes the assumptions underlying most CSP algorithms, and show how solvers can be augmented with an information gathering component that allows openworld constraint satisfaction. We report on experiments that show strong performance of such methods over others where gathering information and solving the CSP are separated.
When shall I fly to New York? Which airline should I choose? How are these related to which airport I arrive at, to how I might travel into the city and to where I choose to stay? Many current and potential applications of agents involve reasoning and communicating about multiple interellated choices. To date however, most proposals for communication in agent systems have provided little or no direct support for the type of communication required by these applications. To address this need, this paper describes the Constraint Choice Language (CCL) -an agent Content Language designed to support agent problem solving by providing explicit representations of choices and choice problems.
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