Carmel Domshlak, Erez Karpas and Shaul Markovitch. Learning to Combine Admissible Heuristics Under Bounded Time. In *Proceedings of the ICAPS 2009 Workshop on Planning and Learning*, Thessaloniki, Greece, 2009.

Usually, combining admissible heuristics for optimal search (e.g. by using their maximum) requires computing numerous heuristic estimates at each state. In many cases, the cost of computing these heuristic estimates outweighs the benefit in the reduced number of expanded states. If only state expan- sions are considered, this is a good option. However, if time is of the essence, we can do better than that. We propose a novel method that reduces the cost of combining admissible heuristics for optimal planning, while maintaining its bene- fits. We first describe a simplified model for deciding which heuristic is best to compute at each state. We then formulate an active learning approach to decide which heuristic to com- pute at each state, online, during search. The resulting tech- nique, which we call selective max is evaluated empirically, and is shown to outperform each of the individual heuristics that were used, as well as their regular maximum, in terms of number of solved instances and average solution time.

@inproceedings{Domshlak:2009:LCA, Author = {Carmel Domshlak and Erez Karpas and Shaul Markovitch}, Title = {Learning to Combine Admissible Heuristics Under Bounded Time}, Year = {2009}, Booktitle = {Proceedings of the ICAPS 2009 Workshop on Planning and Learning}, Address = {Thessaloniki, Greece}, Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Karpas-Domshlak-Markovitch-ICAPS2009ws.pdf}, Keywords = {Planning, Speedup Learning, Anytime Algorithms}, Abstract = { Usually, combining admissible heuristics for optimal search (e.g. by using their maximum) requires computing numerous heuristic estimates at each state. In many cases, the cost of computing these heuristic estimates outweighs the benefit in the reduced number of expanded states. If only state expan- sions are considered, this is a good option. However, if time is of the essence, we can do better than that. We propose a novel method that reduces the cost of combining admissible heuristics for optimal planning, while maintaining its bene- fits. We first describe a simplified model for deciding which heuristic is best to compute at each state. We then formulate an active learning approach to decide which heuristic to com- pute at each state, online, during search. The resulting tech- nique, which we call selective max is evaluated empirically, and is shown to outperform each of the individual heuristics that were used, as well as their regular maximum, in terms of number of solved instances and average solution time. } }