Merge-and-Shrink Task Reformulation for Classical Planning

Merge-and-Shrink Task Reformulation for Classical Planning

Álvaro Torralba, Silvan Sievers

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 5644-5652. https://doi.org/10.24963/ijcai.2019/783

The performance of domain-independent planning systems heavily depends on how the planning task has been modeled. This makes task reformulation an important tool to get rid of unnecessary complexity and increase the robustness of planners with respect to the model chosen by the user. In this paper, we represent tasks as factored transition systems (FTS), and use the merge-and-shrink (M&S) framework for task reformulation for optimal and satisficing planning. We prove that the flexibility of the underlying representation makes the M&S reformulation methods more powerful than the counterparts based on the more popular finite-domain representation. We adapt delete-relaxation and M&S heuristics to work on the FTS representation and evaluate the impact of our reformulation.
Keywords:
Planning and Scheduling: Planning Algorithms
Planning and Scheduling: Search in Planning and Scheduling
Planning and Scheduling: Planning and Scheduling