The FF Heuristic for Lifted Classical Planning

Corrêa, Augusto B.; Pommerening, Florian; Helmert, Malte; Francès, Guillem

doi:10.1609/aaai.v36i9.21206

Cited by 5 publications

(11 citation statements)

References 11 publications

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“…It is easy to see that each action application cannot make the distance of any pair of objects b, c ∈ B larger because P has no delete effects. As s i ⊆ s j , S i is a substructure of any S j for i ≤ j. Consequently, we have δ S j (b, c) ≤ δ S i (b, c) by Lemma 5 (3). Thus the objects get closer when we successively apply the actions from π.…”

Section: Resultsmentioning

confidence: 89%

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Gaifman Graphs in Lifted Planning

Horčík,

Fišer

2023

Frontiers in Artificial Intelligence and Applications

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We introduce the metric induced by Gaifman graphs into lifted planning. We analyze what kind of information this metric carries and how it can be utilized for constructing lifted delete-free relaxation heuristics. In particular, we prove how the action dynamics influence the distances between objects. As a corollary, we derive a lower bound on the length of any plan. Finally, we apply our theoretical findings on the Gaifman graphs to improve the delete-free relaxation heuristics induced by PDDL homomorphisms.

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Section: Resultsmentioning

confidence: 89%

“…For instance, by modifying the Datalog approach from the reachability analysis, the paper [1] showed how to compute h max , h add heuristics on the lifted level. They extended their result also to the FF heuristic [3].…”

Section: Introductionmentioning

confidence: 85%

Gaifman Graphs in Lifted Planning

Horčík,

Fišer

2023

Frontiers in Artificial Intelligence and Applications

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“…For example, rnd-g-95% will often preserve all objects in the goal and map all other objects to a single non-goal object per type. As a baseline for optimal planning, we use lifted blind search (bl), the PowerLifted planner with A and the lifted h max heuristic (pl) (Corrêa et al 2020(Corrêa et al , 2021, and we also compare against the grounded variant of our heuristics (fd) implemented in the Fast Downward planner (Helmert 2006).…”

Section: Optimal Planning Resultsmentioning

confidence: 99%

“…For satisficing planning, we run GBFS with h FF . As baselines, we consider the (fully grounded) Fast Downward planner with GBFS and h FF (fd); the PowerLifted planner with GBFS and the lifted h add (plgb); lazy evaluation with preferred operators and the same heuristic (pl) (both Corrêa et al 2020Corrêa et al , 2021; and GBFS with goal counting breaking ties with the unary-relaxation heuristic (ur) (Lauer et al 2021). The overall performance is not close to the current state-of-the-art satisficing lifted planners pl and ur.…”

Section: Satisficing Planning Resultsmentioning

confidence: 99%

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Homomorphisms of Lifted Planning Tasks: The Case for Delete-Free Relaxation Heuristics

Horčı́k

Fišer

Torralba

2022

AAAI

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Classical planning tasks are modelled in PDDL which is a schematic language based on first-order logic. Most of the current planners turn this lifted representation into a propositional one via a grounding process. However, grounding may cause an exponential blowup. Therefore it is important to investigate methods for searching for plans on the lifted level. To build a lifted state-based planner, it is necessary to invent lifted heuristics. We introduce maps between PDDL tasks preserving plans allowing to transform a PDDL task into a smaller one. We propose a novel method for computing lifted (admissible) delete-free relaxed heuristics via grounding of the smaller task and computing the (admissible) delete-free relaxed heuristics there. This allows us to transfer the knowledge about relaxed heuristics from the grounded level to the lifted level.

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Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs

Rankooh¹,

Janhunen²

2023

Theory and Practice of Logic Programming

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We establish a novel relation between delete-free planning, an important task for the AI planning community also known as relaxed planning, and logic programming. We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be bijectively captured with stable models of a logic program describing the corresponding relaxed planning problem. We also consider the supported model semantics of logic programs, and introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models. Our experimental results show that these new encodings can provide major performance gain when computing optimal relaxed plans, with our diagnostic encoding outperforming state-of-the-art approaches to relaxed planning regardless of the given time limit when measured on a wide collection of STRIPS planning benchmarks.

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The FF Heuristic for Lifted Classical Planning

Cited by 5 publications

References 11 publications

Gaifman Graphs in Lifted Planning

Gaifman Graphs in Lifted Planning

Homomorphisms of Lifted Planning Tasks: The Case for Delete-Free Relaxation Heuristics

Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs

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