Abstract
Heuristic search has been widely applied to classical planning and has proven its efficiency. Even GraphPlan can be interpreted as a heuristic planner. Good heuristics can generally be computed by solving a relaxed problem, but it may be difficult to take into account enough constraints with a fast computation method: The relaxed problem should not make too strong assumptions about the independence of subgoals. Starting from the idea that state-of-the-art heuristics suffer from the difficulty to take some interactions into account, we propose a new approach to control the amount and nature of the constraints taken into account during a reachability analysis process. We formalize search space splitting as a general framework allowing to neglect or take into account a controlled amount of dependences between sub-sets of the reachable space. We show how this reachability analysis can be used to compute a range of heuristics. Experiments are presented and discussed.
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Zemali, Y. (2005). Controlled Reachability Analysis in AI Planning: Theory and Practice. In: Furbach, U. (eds) KI 2005: Advances in Artificial Intelligence. KI 2005. Lecture Notes in Computer Science(), vol 3698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551263_22
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DOI: https://doi.org/10.1007/11551263_22
Publisher Name: Springer, Berlin, Heidelberg
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