DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization Problems

The Upper Confidence Bounds (UCB) algorithm is a well-known near-optimal strategy for the stochastic multi-armed bandit problem. Its extensions to trees, such as the Upper Confidence Tree (UCT) algorithm, have resulted in good so-lutions to the problem of Go. This paper introduces DUCT, a distributed algorithm inspired by UCT, for solving Dis-tributed Constraint Optimization Problems (DCOP). Bounds on the solution quality are provided, and experiments show that, compared to existing DCOP approaches, DUCT is able to solve very large problems much more efficiently, or to find significantly higher quality solutions