Endomorphisms of Lifted Planning Problems

Horčı́k, Rostislav; Fišer, Daniel

doi:10.1609/icaps.v31i1.15960

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Although progress in this area was slow during the early 2000s, recent work showed its usefulness in hard-to-ground benchmarks (Corrêa et al 2021;Lauer et al 2021;Wichlacz, Höller, and Hoffmann 2022;Höller and Behnke 2022;Shaik and van de Pol 2022;Horčík, Fišer, and Torralba 2022;Ståhlberg 2023). We used the Powerlifted planner, but other lifted heuristic search planners could be extended to deal with object creation (e.g., Horčík and Fišer 2021).…”

Section: Related Workmentioning

confidence: 99%

Planning with Object Creation

Corrêa,

De Giacomo,

Helmert

et al. 2024

ICAPS

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Classical planning problems are defined using some specification language, such as PDDL. The domain expert defines action schemas, objects, the initial state, and the goal. One key aspect of PDDL is that the set of objects cannot be modified during plan execution. While this is fine in many domains, sometimes it makes modeling more complicated. This may impact the performance of planners, and it requires the domain expert to bound the number of required objects beforehand, which can be a challenge. We introduce an extension to the classical planning formalism, where action effects can create and remove objects. This problem is semi-decidable, but it becomes decidable if we can bound the number of objects in any given state, even though the state space is still infinite. On the practical side, we extend the Powerlifted planning system to support this PDDL extension. Our results show that this extension improves the performance of Powerlifted while supporting more natural PDDL models.

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Section: Related Workmentioning

confidence: 99%

Planning with Object Creation

Corrêa,

De Giacomo,

Helmert

et al. 2024

ICAPS

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“…The focus of learning for planning is to learn domain knowledge in an automated, domainindependent fashion in order to improve the computation and/or quality of plans. Recent examples of learning for planning methods using DL include learning policies (Toyer et al 2018;Groshev et al 2018;Garg, Bajpai, and Mausam 2019;Rivlin, Hazan, and Karpas 2020;Silver et al 2024), heuristics (Shen, Trevizan, and Thiébaux 2020;Karia and Srivastava 2021) and heuristic proxies (Shen et al 2019;Ferber et al 2022;Chrestien et al 2023), with more recent architectures motivated by theory Geffner 2022, 2023;Mao et al 2023;Horcik and Šír 2024). However, learning for planning is not a new field and works capable of learning similar domain knowledge using classical statistical machine learning (SML) methods predate DL.…”

Section: Introductionmentioning

confidence: 99%

Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning

Chen,

Trevizan,

Thiébaux

2024

ICAPS

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Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance. In this work, we construct novel graph representations of lifted planning tasks and use the WL algorithm to generate features from them. These features are used with classical machine learning methods which have up to 2 orders of magnitude fewer parameters and train up to 3 orders of magnitude faster than the state-of-the-art deep learning for planning models. Our novel approach, WL-GOOSE, reliably learns heuristics from scratch and outperforms the hFF heuristic in a fair competition setting. It also outperforms or ties with LAMA on 4 out of 10 domains on coverage and 7 out of 10 domains on plan quality. WL-GOOSE is the first learning for planning model which achieves these feats. Furthermore, we study the connections between our novel WL feature generation method, previous theoretically flavoured learning architectures, and Description Logic Features for planning.

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“…This focus is historically motivated, as domain-independent planning engines traditionally ground the lifted PDDL representation. However, as engines capable of reasoning with lifted or partially ground representation are gaining momentum (see for instance [Horčík andFišer, 2021, Corrêa et al, 2020]), reformulations that are effective also on lifted models can and should be investigated more convincingly.…”

mentioning

confidence: 99%

Reformulation Techniques for Automated Planning: A Systematic Review

Alarnaouti¹,

Baryannis²,

Vallati³

2023

Preprint

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Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesise a solution plan.Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation.Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.

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Endomorphisms of Lifted Planning Problems

Cited by 4 publications

References 23 publications

Planning with Object Creation

Planning with Object Creation

Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning

Reformulation Techniques for Automated Planning: A Systematic Review

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