Numeric Planning via Abstraction and Policy Guided Search

Numeric Planning via Abstraction and Policy Guided Search

León Illanes, Sheila A. McIlraith

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 4338-4345. https://doi.org/10.24963/ijcai.2017/606

The real-world application of planning techniques often requires models with numeric fluents. However, these fluents are not directly supported by most planners and heuristics. We describe a family of planning algorithms that takes a numeric planning problem and produces an abstracted representation that can be solved using any classical planner. The resulting abstract plan is generalized into a policy and then used to guide the search in the original numeric domain. We prove that our approach is sound, and we evaluate it on a set of standard benchmarks. We show that it can provide competitive performance when compared to other well-known algorithms for numeric planning, and a significant performance improvement in certain domains.
Keywords:
Planning and Scheduling: Planning Algorithms
Planning and Scheduling: Search in Planning and Scheduling