Symbolic perimeter abstraction heuristics for cost-optimal planning

Torralba, Álvaro; López, Carlos Linares; Borrajo, Daniel

doi:10.1016/j.artint.2018.02.002

Cited by 18 publications

(43 citation statements)

References 61 publications

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“…• The two most competitive unsolvability merge-andshrink (M&S) abstractions [Hoffmann et al, 2014;Torralba et al, 2016]: MSp which computes the perfect dead-end detector ∆ * , recognizing all dead-ends; and MSa which imposes a bound on the abstraction size, and hence approximates ∆ * . We include MSp for reference only, and do not use it for computing ∆-traps.…”

Section: Methodsmentioning

confidence: 99%

“…After initial works [Bäckström et al, 2013;, a wealth of techniques participated in the inaugural Unsolvability International Planning Competition (UIPC'16) (e. g., [Torralba and Alcázar, 2013;Domshlak et al, 2015;Torralba et al, 2016;Steinmetz and Hoffmann, 2016a;Torralba, 2016;Gnad et al, 2016]). One major strand of these works designs what we will refer to as dead-end detectors ∆: effectively testable criteria sufficient for unsolvability, designed to be called on every state during search, serving to prune those dead-end states detected.…”

Section: Introductionmentioning

confidence: 99%

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Search and Learn: On Dead-End Detectors, the Traps they Set, and Trap Learning

Steinmetz

Hoffmann

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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A key technique for proving unsolvability in classical planning are dead-end detectors ∆: effectively testable criteria sufficient for unsolvability, pruning (some) unsolvable states during search. Related to this, a recent proposal is the identification of traps prior to search, compact representations of non-goal state sets T that cannot be escaped. Here, we create new synergy across these ideas. We define a generalized concept of traps, relative to a given dead-end detector ∆, where T can be escaped, but only into dead-end states detected by ∆. We show how to learn compact representations of such T during search, extending the reach of ∆. Our experiments show that this can be quite beneficial. It improves coverage for many unsolvable benchmark planning domains and dead-end detectors ∆, in particular on resource-constrained domains where it outperforms the state of the art.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Search and Learn: On Dead-End Detectors, the Traps they Set, and Trap Learning

Steinmetz

Hoffmann

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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“…Symbolic backward search (regression) can be realized by starting with the goal states, applying the preimage operation until the initial state is found. The combination of these two searches forms a bidirectional search used by most modern symbolic planners (Torralba 2015).…”

Section: Symbolic Searchmentioning

confidence: 99%

“…Up to this point, we have only considered actions with nonzero costs. In symbolic search, before applying non-zero cost actions, it is necessary to perform an additional SYM-DIJ with zero cost actions in order to obtain all states reachable with certain costs (Torralba 2015). We will illustrate this with Example 1 depicted in Figure 1, where this time the pickup and drop actions cost 0 and the move action costs 1.…”

Section: Actions With Zero Costsmentioning

confidence: 99%

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Symbolic Top-k Planning

Speck

Mattmüller

Nebel

2020

AAAI

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The objective of top-k planning is to determine a set of k different plans with lowest cost for a given planning task. In practice, such a set of best plans can be preferred to a single best plan generated by ordinary optimal planners, as it allows the user to choose between different alternatives and thus take into account preferences that may be difficult to model. In this paper we show that, in general, the decision problem version of top-k planning is PSPACE-complete, as is the decision problem version of ordinary classical planning. This does not hold for polynomially bounded plans for which the decision problem turns out to be PP-hard, while the ordinary case is NP-hard. We present a novel approach to top-k planning, called sym-k, which is based on symbolic search, and prove that sym-k is sound and complete. Our empirical analysis shows that sym-k exceeds the current state of the art for both small and large k.

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