Tracking Branches in Trees - A Propositional Encoding for Solving Partially-Ordered HTN Planning Problems

Behnke, Gregor; Höller, Daniel; Biundo, Susanne

doi:10.1109/ictai.2018.00022

Cited by 15 publications

(18 citation statements)

References 10 publications

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“…We implemented the given techniques within the PANDA planning framework using its implementation of a propositional encoding for HTN planning [Behnke et al, 2019a;Behnke et al, 2018b;Behnke et al, 2018a]. The code can be downloaded at https://www.uni-ulm.de/en/in/ki/panda/.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, simply using an optimal classical planner does not yield an optimal HTN planner. Transformation into propositional logic We recently proposed a translation of HTN problems into propositional logic, bounding the decomposition depth of the problem [Behnke et al, 2018a;Behnke et al, 2018b;Behnke et al, 2019a]. We construct a formula for a given HTN planning problem P and a depth bound K that is satisfiable if and only if P has a solution with a decomposition depth ≤ K. As this formula represents exactly all plans with decompositions depth ≤ K, it accounts for plans up to the maximum length achievable by decompositions of depth ≤ K. While heuristic searchbased techniques have to consider the interaction between constraints imposed via the actions' state-transition semantics and the decompositional structure of the problem explicitly, a SAT-based approach translates the whole problem into a single homogeneous representation.…”

Section: Htn Planningmentioning

confidence: 99%

“…Several new approaches to solve HTN planning problems have been introduced over the last years and increased the overall performance of the systems in that field. These new techniques range from heuristic search in plan space , heuristic progression search , over the translation into classical planning problems [Alford et al, 2016], to translations into propositional logic [Behnke et al, 2019a;Behnke et al, 2018b;Behnke et al, 2018a;Schreiber et al, 2019]. Both translation-based approaches bound the original problem to be able to translate the undecidable HTN planning problem into a decidable one.…”

Section: Introductionmentioning

confidence: 99%

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Finding Optimal Solutions in HTN Planning - A SAT-based Approach

Behnke

Höller

Biundo

2019

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

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Over the last years, several new approaches to Hierarchical Task Network (HTN) planning have been proposed that increased the overall performance of HTN planners. However, the focus has been on agile planning - on finding a solution as quickly as possible. Little work has been done on finding optimal plans. We show how the currently best-performing approach to HTN planning - the translation into propositional logic - can be utilised to find optimal plans. Such SAT-based planners usually bound the HTN problem to a certain depth of decomposition and then translate the problem into a propositional formula. To generate optimal plans, the length of the solution has to be bounded instead of the decomposition depth. We show the relationship between these bounds and how it can be handled algorithmically. Based on this, we propose an optimal SAT-based HTN planner and show that it performs favourably on a benchmark set.

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Section: Discussionmentioning

confidence: 99%

Section: Htn Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finding Optimal Solutions in HTN Planning - A SAT-based Approach

Behnke

Höller

Biundo

2019

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

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“…• Totally ordered problems [8] • Partially ordered problems [9][10][11] Mixed Initiative Planning (Sec. 3.2)…”

Section: The Problem Definition Language Hddlmentioning

confidence: 99%

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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“…Others have been revisited, e.g. the translation to propositional logic (Behnke, Höller, and Biundo 2018a;2018b;2019a;2019b;Schreiber et al 2019). In contrast to many earlier systems, these systems can be considered to be domain-independent, they do not rely on hand-tailored advice to solve problems, but only on their solving techniques.…”

Section: Introductionmentioning

confidence: 99%

HDDL: An Extension to PDDL for Expressing Hierarchical Planning Problems

Höller

Behnke

Bercher

et al. 2020

AAAI

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The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and – much more important – also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems.

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Tracking Branches in Trees - A Propositional Encoding for Solving Partially-Ordered HTN Planning Problems

Cited by 15 publications

References 10 publications

Finding Optimal Solutions in HTN Planning - A SAT-based Approach

Finding Optimal Solutions in HTN Planning - A SAT-based Approach

The PANDA Framework for Hierarchical Planning

HDDL: An Extension to PDDL for Expressing Hierarchical Planning Problems

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