Exploiting Problem Structure as a Search Heuristic

Recent empirical and theoretical studies have shown that simple parameters characterizing constraint satisfaction problems predict whether they have a solution and the cost to solve them, on average. This paper examines the effectiveness of using these predictions as a heuristic for solving the graph coloring problem. Specifically, by adding some global information on the consequences of various choices, the use of these parameters can reduce the search required to find a solution. Current limitations of this approach, due to the high variance associated with the predictions, are also presented. More generally, observations of universal behaviors analogous to physical phase transitions can be applied to improve search methods.