Subset-Saturated Transition Cost Partitioning

Drexler, Dominik; Seipp, Jendrik; Speck, D. R.

doi:10.1609/icaps.v31i1.15955

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…A common approach to solving classical planning tasks optimally is A * search (Hart, Nilsson, and Raphael 1968) with an admissible heuristic (e.g., Helmert and Domshlak 2009;Karpas and Domshlak 2009;Katz and Domshlak 2010;Pommerening et al 2015;Sievers and Helmert 2021). Heuristics based on abstractions of the planning task have been particularly successful (e.g., Franco et al 2017;Seipp 2019;Drexler, Seipp, and Speck 2021;Kreft et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Efficiently Computing Transitions in Cartesian Abstractions

Seipp

2024

ICAPS

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Counterexample-guided Cartesian abstraction refinement yields strong heuristics for optimal classical planning. The approach iteratively finds a new abstract solution, checks where it fails for the original task and refines the abstraction to avoid the same failure in subsequent iterations. The main bottleneck of this refinement loop is the memory needed for storing all abstract transitions. To address this issue, we introduce an algorithm that efficiently computes abstract transitions on demand. This drastically reduces the memory consumption and allows us to solve tasks during the refinement loop and during the search that were previously out of reach.

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Section: Introductionmentioning

confidence: 99%

Efficiently Computing Transitions in Cartesian Abstractions

Seipp

2024

ICAPS

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“…As a result, previous research has leveraged state sampling either before [14,27,25,5,19] or during [22] the search. These sampled states serve as a basis for computing cost partitioning heuristics, which are then maximized over during the search process.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis for Saturated Post-Hoc Optimization in Classical Planning

Höft,

Speck,

Seipp

2023

Frontiers in Artificial Intelligence and Applications

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Cost partitioning is the foundation of today’s strongest heuristics for optimal classical planning. However, computing a cost partitioning for each evaluated state is prohibitively expensive in practice. Thus, existing approaches make an approximation and compute a cost partitioning only for a set of sampled states, and then reuse the resulting heuristics for all other states evaluated during the search. In this paper, we present exact methods for cost partitioning heuristics based on linear programming that fully preserve heuristic accuracy while minimizing computational cost. Specifically, we focus on saturated post-hoc optimization and establish several sufficient conditions for when reusing a cost partitioning computed for one state preserves the estimates for other states, mainly based on a sensitivity analysis of the underlying linear program. Our experiments demonstrate that our theoretical results transfer into practice, and that our exact cost partitioning algorithms are competitive with the strongest approximations currently available, while usually requiring fewer linear program evaluations.

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“…A prominent solution method for this problem is A * (Hart, Nilsson, and Raphael 1968) with an admissible heuristic (Pearl 1984). Cartesian abstractions (Ball, Podelski, and Rajamani 2001) represent a smaller and simpler version of the given concrete task and provide a way to obtain admissible heuristics by either considering a single abstraction or combining multiple abstractions (Katz and Domshlak 2008;Yang et al 2008;Pommerening et al 2015;Seipp, Keller, and Helmert 2020;Drexler, Seipp, and Speck 2021).…”

Section: Introductionmentioning

confidence: 99%

New Refinement Strategies for Cartesian Abstractions

Speck

Seipp

2022

ICAPS

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Cartesian counterexample-guided abstraction refinement (CEGAR) yields strong heuristics for optimal classical planning. CEGAR repeatedly finds counterexamples, i.e., abstract plans that fail for the concrete task. Although there are usually many such abstract plans to choose from, the refinement strategy from previous work is to choose an arbitrary optimal one. In this work, we show that an informed refinement strategy is critical in theory and practice. We demonstrate that it is possible to execute all optimal abstract plans in the concrete task simultaneously, and present methods to minimize the time and number of refinement steps until we find a concrete solution. The resulting algorithm solves more tasks than the previous state of the art for Cartesian CEGAR, both during refinement and when used as a heuristic in an A* search.

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Subset-Saturated Transition Cost Partitioning

Cited by 3 publications

References 18 publications

Efficiently Computing Transitions in Cartesian Abstractions

Efficiently Computing Transitions in Cartesian Abstractions

Sensitivity Analysis for Saturated Post-Hoc Optimization in Classical Planning

New Refinement Strategies for Cartesian Abstractions

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