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**1 - 2**of**2**### Optimal Planning and Shortcut Learning: An Unfulfilled Promise

"... An existential optimal landmark is a set of actions, one of which must be used in some optimal plan. Recently, Karpas and Domshlak (2012) introduced a technique for deriving such existential optimal landmarks, which is based on using shortcut rules — rules which take a path, and attempt to find a ch ..."

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An existential optimal landmark is a set of actions, one of which must be used in some optimal plan. Recently, Karpas and Domshlak (2012) introduced a technique for deriving such existential optimal landmarks, which is based on using shortcut rules — rules which take a path, and attempt to find a cheaper path that achieves some of the propositions that the original path achieved. The shortcut rules that were originally used were of a limited form, and only attempted to remove parts of the given path. One would expect that using more sophisticated shortcut rules would result in a more informative heuristic, although possibly at the cost of increased computation time. We show that, somewhat surprisingly, more sophisticated shortcut rules, which are learned online, during search, result in a very small increase in informativeness on IPC benchmarks. Together with the increased computational cost, this leads to a decrease in the number of problems solved, and leaves finding efficient, informative shortcut rules as a standing challenge.

### Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Bridging the Gap Between Refinement and Heuristics in Abstraction

"... There are two major uses of abstraction in planning and search: refinement (where abstract solutions are extended into concrete solutions) and heuristics (where abstract solutions are used to compute heuristics for the original search space). These two approaches are usually viewed as unrelated in t ..."

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There are two major uses of abstraction in planning and search: refinement (where abstract solutions are extended into concrete solutions) and heuristics (where abstract solutions are used to compute heuristics for the original search space). These two approaches are usually viewed as unrelated in the literature. It is reasonable to believe, though, that they are related, since they are both intrinsically based on the structure of abstract search spaces. We take the first steps towards formally investigating their relationships, employing our recently introduced framework for analysing and comparing abstraction methods. By adding some mechanisms for expressing metric properties, we can capture concepts like admissibility and consistency of heuristics. We present an extensive study of how such metric properties relate to the properties in the original framework, revealing a number of connections between the refinement and heuristic approaches. This also provides new insights into, for example, Valtorta’s theorem and spurious states. 1