Analysis of heuristic methods for partial constraint satisfaction problems

Abstract

Problems that do not have complete solutions occur in many areas of application of constraint solving. Heuristic repair methods that have been used successfully on complete CSPs can also be used on overconstrained problems. A difficulty in analyzing their performance is the uncertainty about the goodness of solutions returned in relation to the optimal (best possible) solutions. This difficulty can be overcome by testing these procedures on problems that can be solved by complete methods, which return certifiably optimal solutions. With this experimental strategy, comparative analyses of hill-climbing methods were carried out using anytime curves that could be compared with known optima. In addition, extensive analysis of parameter values for key strategies such as random walk and restarting could be done precisely and efficiently by allowing local search to run until a solution was discovered that was known to be optimal, based on earlier tests with complete methods. An important finding is that a version of min-conflicts that incorporates the random walk strategy, with a good value for the walk probability appears to be as efficient in this domain as several of the more elaborate methods for improving local search that have been proposed in recent years.