Boosting distributed constraint satisfaction

Competition and cooperation can boost the performance of a combinatorial search process. Both can be implemented with a portfolio of algorithms which run in parallel, give hints to each other and compete for being the first to finish and deliver the solution. In this paper we present a new generic framework for the application of algorithms for distributed constraint satisfaction that makes use of both cooperation and competition. This framework improves the performance of two different standard algorithms by one order of magnitude. Furthermore, it can reduce the risk of poor performance by up to three orders of magnitude diminishing the heavy-tailed behaviour of complete distributed search. Moreover it greatly reduces the classical idleness flaw usually observed in distributed tree-based searches. We expect our new methods to be similarly beneficial for any tree-based distributed search and describe ways on how to incorporate them. Remarkably, our ideas while applied to a parallel SAT setting were able to beat divide-and-conquers approaches, and win the gold medal of the parallel track of the 2008 SAT-Race.