Friends or Foes? An AI Planning Perspective on Abstraction and Search

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There is increasing awareness that planning and model checking are closely related fields. Abstraction means to perform search in an over-approximation of the original problem instance, with a potentially much smaller state space. This is the most essential method in model checking. One would expect that it can also be made successful in planning. We show, however, that this is likely to not be the case. The main reason is that, while in model checking one traditionally uses blind search to exhaust the state space and prove the absence of solutions, in planning informed search is used to find solutions. We give an exhaustive theoretical and practical account of the use of abstraction in planning. For all abstraction (over-approximation) methods known in planning, we prove that they cannot improve the best-case behavior of informed search. While this is easy to see for heuristic search, we were quite surprised to find that it also holds, in most cases, for the resolution-style proofs of unsolvability underlying SAT-based optimal planners. This result is potentially relevant also for model checking, where SAT-based techniques have recently been combined with abstraction. Exploring the issue in planning practice, we find that even hand-made abstractions do not tend to improve the performance of planners, unless the attacked task contains huge amounts of irrelevance. We relate these findings to the kinds of application domains that are typically addressed in model checking.