Material Detail

This video was recorded at 21st International Conference on Automated Planning and Scheduling. 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 precomputation, 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.

Disciplines:

• Science and Technology  / Computer Science  / Intelligent Systems