Comparing two approaches to dynamic, distributed constraint satisfaction

It is now fairly well understood that a vast number of AI problems can be formulated as Constraint Satisfaction Problems (CSPs) and striking improvements have been made in solving them using both centralized and distributed methods. However, many real world problems change over time and very little work has been done in developing methods, particularly distributed ones, for solving problems which exhibit this behavior.This paper presents two new protocols for solving dynamic, distributed constraint satisfaction problems which are based on the classic Distributed Breakout Algorithm (DBA) and the Asynchronous Partial Overlay (APO) algorithm. These two new algorithms are compared on a broad class of problems varying the problems' overall difficulty as well as the rate at which they change over time. The results indicate that neither of the algorithms complete dominates the other on all problem types, but that depending on environmental conditions and the needs of the user, one method may be preferable over the other.

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