Tree-REX: SAT-Based Tree Exploration for Efficient and High-Quality HTN Planning

Schreiber, Dominik; Pellier, Damien; Fiorino, Humbert; Balyoˇ, Toma ́s

doi:10.1609/icaps.v29i1.3502

Cited by 6 publications

(18 citation statements)

References 19 publications

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“…To avoid creating too large a formula, the typical approach is to create a sequence F i (P ) of formulae of increasing size and solve them one-by-one until a solution is found. In all currently existing encodings (Schreiber 2021b;Schreiber et al 2019;Behnke, Höller, and Biundo 2018a), the parameter i bounds the maximum depth of decomposition. As such, the formula considers only plans that have a decomposition tree with a maximum depth of i.…”

Section: Sat-based Tohtn Planningmentioning

confidence: 99%

“…We focus on the latter, namely SAT-based TOHTN planning. Recent work proposed two grounded encoding techniques (Schreiber et al 2019;Behnke, Höller, and Biundo 2018a). We don't consider the lifted encoding by Schreiber (2021b) as we restrict ourselves to grounded planning.…”

Section: Introductionmentioning

confidence: 99%

“…The core element of this technique is to compute a representation of all decompositions up to the given depth limit K. For TOHTN, the derivation of a plan via decompositions takes the form of a parse tree for a context-free grammar. As such, the propositional formula has to represent all possible parse trees that have a depth of at most K. For this, two isomorphic representations have been proposed: A common super tree of all possible parse trees (Path Decomposition Tree, PDT) (Behnke, Höller, and Biundo 2018a) and a tape-based layer structure (Schreiber et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…PANDA (Behnke, Höller, and Biundo 2018a) compiles method preconditions into additional actions -which must be represented in the encoding and increase the overall length of the plan. Tree-Rex (Schreiber et al 2019) handles method preconditions separately by checking the method precondition in the state in which we apply the first derived action -which leads to a smaller SAT formula. We generalise this idea to the concept of block compression.…”

Section: Introductionmentioning

confidence: 99%

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Block Compression and Invariant Pruning for SAT-based Totally-Ordered HTN Planning

Behnke

2021

ICAPS

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Translations into propositional logic are currently one of the most efficient techniques for solving Totally-Ordered HTN planning problems. The two current encodings both iterate over the maximum allowed depth of decomposition. Given this depth, they compute a tree that represents all possible decompositions up to this depth. Based on this tree, a formula in propositional logic is created. We show that much of the computed tree is actually useless as it cannot possibly belong to a solution. We provide a technique for removing (parts of) these useless structures using state invariants. We further show that is often not necessary to encode all leafs of this tree as separate timesteps, as the prior encodings did. Instead, we can compress the leafs into blocks and encode all leafs of a block at one timestep. We show that these changes provide an improvement over the state-of-the-art in HTN planning.

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Section: Sat-based Tohtn Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Block Compression and Invariant Pruning for SAT-based Totally-Ordered HTN Planning

Behnke

2021

ICAPS

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“…Other techniques have been extended, e.g. propositional encodings for SATbased planners Biundo 2018, 2019;Schreiber et al 2019), or IP encodings for heuristics (Höller, Bercher, and Behnke 2020). Alford et al (2009; introduced translations from HTN problems into classical problems by adding a new part to the state of the problem representing the decomposition process.…”

Section: Introductionmentioning

confidence: 99%

Translating Totally Ordered HTN Planning Problems to Classical Planning Problems Using Regular Approximation of Context-Free Languages

Höller

2021

ICAPS

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There have been several approaches to use techniques from classical planning in HTN planning. While a direct translation is in general not possible due to the different expressiveness, there have been translations of bounded HTN problems and approaches to use classical heuristics in HTN search procedures. In this paper, we introduce a different approach. We exploit methods from the field of Computational Linguistics introduced to approximate Context-Free Languages by Finite Automata. We use them to approximate the decomposition structure of Totally Ordered (TO) HTN planning problems by classical problems. The resulting problem can then be solved using standard classical planning systems. A subset of TOHTN problems can be translated exactly, i.e., without changing the set of solutions. For problems where an approximation is necessary, we use an overapproximation, i.e., the set of solutions to the classical problem is a superset of that of the HTN problem. We then use plan verification to check whether a solution is valid and thus obtain a sound and complete overall approach. The resulting system outperforms the state of the art on the IPC 2020 benchmark set in terms of coverage.

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The PANDA Framework for Hierarchical Planning

et al. 2021

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During the last years, much progress has been made in hierarchical planning towards domain-independent systems that come with sophisticated techniques to solve planning problems instead of relying on advice in the input model. Several of these novel methods have been integrated into the PANDA framework, which is a software system to reason about hierarchical planning tasks. Besides solvers for planning problems based on plan space search, progression search, and translation to propositional logic, it also includes techniques for related problems like plan repair, plan and goal recognition, or plan verification. These various techniques share a common infrastructure, like e.g. a standard input language or components for grounding and reachability analysis. This article gives an overview over the PANDA framework, introduces the basic techniques from a high level perspective, and surveys the literature describing the diverse components in detail.

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Tree-REX: SAT-Based Tree Exploration for Efficient and High-Quality HTN Planning

Cited by 6 publications

References 19 publications

Block Compression and Invariant Pruning for SAT-based Totally-Ordered HTN Planning

Block Compression and Invariant Pruning for SAT-based Totally-Ordered HTN Planning

Translating Totally Ordered HTN Planning Problems to Classical Planning Problems Using Regular Approximation of Context-Free Languages

The PANDA Framework for Hierarchical Planning

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