MS-Lite: A Lightweight, Complementary Merge-and-Shrink Method

Fan, Gaojian; Holte, Robert C.; Müller, Martin

doi:10.1609/icaps.v28i1.13885

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…Our primary experiments began with the 747 IPC problems solved optimally by the lite-enhanced DM-HQ algorithm (Fan, Holte, and Mueller 2018) from 39 domains and the 110 solved problems from 6 domains in the 2018 IPC optimal track. 2 Only 568 of these problems (538 from pre-2018 IPCs and 30 from the 2018 IPC) were solved within our time and memory limits by all combinations of W -values and heuristics.…”

Section: Methodsmentioning

confidence: 99%

Error Analysis and Correction for Weighted A*’s Suboptimality

Holte¹,

Majadas²,

Pozanco³

et al. 2021

SOCS

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Weighted A* (wA*) is a widely used algorithm for rapidly, but suboptimally, solving planning and search problems. The cost of the solution it produces is guaranteed to be at most W times the optimal solution cost, where W is the weight wA* uses in prioritizing open nodes. W is therefore a suboptimality bound for the solution produced by wA*. There is broad consensus that this bound is not very accurate, that the actual suboptimality of wA*'s solution is often much less than W times optimal. However, there is very little published evidence supporting that view, and no existing explanation of why W is a poor bound. This paper fills in these gaps in the literature. We begin with a large-scale experiment demonstrating that, across a wide variety of domains and heuristics for those domains, W is indeed very often far from the true suboptimality of wA*'s solution. We then analytically identify the potential sources of error. Finally, we present a practical method for correcting for two of these sources of error and experimentally show that the corrections frequently eliminate much of the error.

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Section: Methodsmentioning

confidence: 99%

Error Analysis and Correction for Weighted A*’s Suboptimality

Holte¹,

Majadas²,

Pozanco³

et al. 2021

SOCS

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show abstract

“…The method proposed in this paper is based on the mergeand-shrink literature (Helmert et al 2007;Nissim, Hoffmann, and Helmert 2011;Helmert 2006;Fan, Holte, and Müller 2018), which has focused on classical planning problems so far. Our method also builds on recent advances in temporal planning, particularly the work on partial-order search-based planners (Coles et al 2010;Coles and Coles 2016;Benton, Coles, and Coles 2012).…”

Section: Related Workmentioning

confidence: 99%

“…As shrinking strategies we evaluate bimisulation ("bisim") and minimal h-preserving shrinking ("hshrink"). hshrink is an h-preserving shrink where all same-formula states are aggregated in a single state (Fan, Holte, and Müller 2018). The implementation of bisimulation was ported from FastDownward into OPTIC.…”

Section: Merge-and-shrink Strategymentioning

confidence: 99%

“…Thus the use of plan equivalency and memoization strategies, as well as tight heuristics, is crucial for efficient temporal planning. In this paper we adapt the work on merge-and-shrink abstraction heuristics (Helmert et al 2007;Nissim, Hoffmann, and Helmert 2011;Fan, Holte, and Müller 2018) into PDDL temporal planning, and use these to improve the efficiency of two families of temporal planners: partial-order and total-order planners.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we build on the work of temporal mutex analysis (Bernardini and Smith 2011) and (classical) merge-and-shrink abstractions (Helmert et al 2007;Nissim, Hoffmann, and Helmert 2011;Fan, Holte, and Müller 2018) to generate merge-and-shrink abstraction heuristics for temporal planning. These abstractions are built by successively taking the product of (single-variable) transition graphs and then shrinking the state-space by "collapsing" abstract states based on their lower-bound estimates of goalmakespan.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Merge and Shrink Abstractions for Temporal Planning

Brandão

Coles

Hoffmann

2022

ICAPS

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Temporal planning is a hard problem that requires good heuristic and memoization strategies to solve efficiently. Merge-and-shrink abstractions have been shown to serve as effective heuristics for classical planning, but they have not yet been applied to temporal planning. Currently, it is still unclear how to implement merge-and-shrink in the temporal domain and how effective the method is in this setting. In this paper we propose a method to compute merge-and-shrink abstractions for temporal planning, applicable to both partial- and total-order temporal planners. The method relies on pre-computing heuristics as formulas of temporal variables that are evaluated at search time, and it allows to use standard shrinking strategies and label reduction. Compared to state-of-the-art Relaxed Planning Graph heuristics, we show that the method leads to improvements in coverage, computation time, and number of explored nodes to solve optimal problems, as well as leading to improvements in unsolvability-proving of problems with deadlines.

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Merge-and-Shrink Heuristics for Classical Planning: Efficient Implementation and Partial Abstractions

Sievers

2021

SOCS

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Merge-and-shrink heuristics are a successful class of abstraction heuristics used for optimal classical planning. With the recent addition of generalized label reduction, merge-and-shrink can be understood as an algorithm framework that repeatedly applies transformations to a factored representation of a given planning task to compute an abstraction. In this paper, we describe an efficient implementation of the framework and its transformations, comparing it to its previous implementation in Fast Downward. We further discuss partial merge-and-shrink abstractions that do not consider all aspects of the concrete state space. To compute such partial abstractions, we stop the merge-and-shrink computation early by imposing simple limits on the resource consumption of the algorithm. Our evaluation shows that the efficient implementation indeed improves over the previous one, and that partial merge-and-shrink abstractions further push the efficiency of merge-and-shrink planners.

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MS-Lite: A Lightweight, Complementary Merge-and-Shrink Method

Cited by 4 publications

References 7 publications

Error Analysis and Correction for Weighted A*’s Suboptimality

Error Analysis and Correction for Weighted A*’s Suboptimality

Merge and Shrink Abstractions for Temporal Planning

Merge-and-Shrink Heuristics for Classical Planning: Efficient Implementation and Partial Abstractions

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