Numeric Planning via Search Space Abstraction

Many real-world planning problems are best modeled as infinite search space problems, using numeric fluents. Unfortunately, most planners and planning heuristics do not directly support such fluents. We propose a search space abstraction technique that compiles a planning problem with numeric fluents into a finite state propositional planning problem. To account for the loss of precision resulting from the abstraction, we leverage a policy repair technique used for non-deterministic planning and describe a new algorithm for planning with numeric fluents. We evaluate our approach on a set of benchmarks and compare it to state-of-theart planners that deal with numeric fluents.