Online Saturated Cost Partitioning for Classical Planning
Keywords:Classical Planning Techniques And Analysis
AbstractCost partitioning is a general method for admissibly summing up heuristic estimates for optimal state-space search. Most cost partitioning algorithms can optimize the resulting cost-partitioned heuristic for a specific state. Since computing a new cost-partitioned heuristic for each evaluated state is usually too expensive in practice, the strongest planners based on cost partitioning over abstraction heuristics precompute a set of cost-partitioned heuristics before the search and maximize over their estimates during the search. This makes state evaluations very fast, but since there is no better termination criterion than a time limit, it requires a long precomputation phase, even for the simplest planning tasks. A prototypical example for this is the Scorpion planner which computes saturated cost partitionings over abstraction heuristics offline before the search. Using Scorpion as a case study, we show that by incrementally extending the set of cost-partitioned heuristics online during the search, we drastically speed up the planning process and even often solve more tasks.
How to Cite
Seipp, J. (2021). Online Saturated Cost Partitioning for Classical Planning. Proceedings of the International Conference on Automated Planning and Scheduling, 31(1), 317-321. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/15976