Learning Inadmissible Heuristics During Search

Suboptimal search algorithms offer shorter solving times by sacrificing guaranteed solution optimality. While optimal search algorithms like A* and IDA* require admissible heuristics, suboptimal search algorithms need not constrain their guidance in this way. Previous work has explored using off-line training to transform admissible heuristics into more effective inadmissible ones. In this paper we demonstrate that this transformation can be performed on-line, during search. In addition to not requiring training instances and extensive pre-computation, an on-line approach allows the learned heuristic to be tailored to a specific problem instance. We evaluate our techniques in four different benchmark domains using both greedy best-first search and bounded suboptimal search. We find that heuristics learned on-line result in both faster search and better solutions while relying only on information readily available in any best-first search.