Counterexample-guided Abstraction Refinement for Classical Planning Master’s Thesis

Counterexample-guided abstraction refinement (CEGAR) is amethodological framework for incrementally computing abstractions of transition systems. We propose a CEGAR algorithm for computing abstraction heuristics for optimal classical planning. Starting from a coarse abstraction of the planning task, we iteratively compute an optimal abstract solution, check if and why it fails for the concrete planning task and refine the abstraction so that the same failure cannot occur in future iterations. A key ingredient of our approach is a novel class of abstractions for classical planning tasks that admits efficient and very fine-grained refinement. Our implementation performs tens of thousands of refinement steps in a few minutes and produces heuristics that are often significantly more accurate than pattern database heuristics of the same size. Our second contribution is the application of those heuristics for heuristic improvement during search. We show how refining the abstraction online when the search errs reduces the number of expanded nodes.