OMTPlan: A Tool for Optimal Planning Modulo Theories

Leofante, Francesco

doi:10.3233/sat-220001

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…The second team submitted OMTPlan , which exposed two different SMT‐based planners. Those are described in detail in a recent publication (Leofante 2023). All participants have been given a GitHub account that they used to upload the source code of the planners.…”

Section: Numeric Trackmentioning

confidence: 99%

The 2023 International Planning Competition

Taitler,

Alford,

Espasa

et al. 2024

AI Magazine

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In this article, we present an overview of the 2023 International Planning Competition. It featured five distinct tracks designed to assess cutting‐edge methods and explore the frontiers of planning within these settings: the classical (deterministic) track, the numeric track, the Hierarchical Task Networks (HTN) track, the learning track, and the probabilistic and reinforcement learning track. Each of these tracks evaluated planning methodologies through one or more subtracks, with the goal of pushing the boundaries of current planner performance. To achieve this objective, the competition introduced a combination of well‐established challenges and entirely novel ones. Within this article, each track offers an exploration of its historical context, justifies its relevance within the planning landscape, discusses emerging domains and trends, elucidates the evaluation methodology, and ultimately presents the results.

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Section: Numeric Trackmentioning

confidence: 99%

The 2023 International Planning Competition

Taitler,

Alford,

Espasa

et al. 2024

AI Magazine

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show abstract

“…An important limit of existing work on generalised planning is that it only allows for primitive forms of quantitative information to be modelled, 1 even though such information is core to many real world problems -for example, modelling a delivery robot requires modelling how much weight it can hold, and modelling flights require reasoning about the the product of distance travelled and fuel consumption per unit of distance. This is despite the existence of the vibrant field of Numeric planning, which extends classical planning formalisms to allow modelling numeric fluents, conditions and effects (Fox and Long 2003), and typically handles them using new heuristic search, optimisation, or satisfiability modulo theory based techniques (Hoffmann 2003;Coles et al 2013;Scala et al 2016aScala et al ,b, 2020Kuroiwa et al 2022;Leofante 2023).…”

Section: Introductionmentioning

confidence: 99%

Learning Generalised Policies for Numeric Planning

Wang,

Thiébaux

2024

ICAPS

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We extend Action Schema Networks (ASNets) to learn generalised policies for numeric planning, which features quantitative numeric state variables, preconditions and effects. We propose a neural network architecture that can reason about the numeric variables both directly and in context of other variables. We also develop a dynamic exploration algorithm for more efficient training, by better balancing the exploration versus learning tradeoff to account for the greater computational demand of numeric teacher planners. Experimentally, we find that the learned generalised policies are capable of outperforming traditional numeric planners on some domains, and the dynamic exploration algorithm to be on average much faster at learning effective generalised policies than the original ASNets training algorithm.

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OMTPlan: A Tool for Optimal Planning Modulo Theories

Cited by 2 publications

References 16 publications

The 2023 International Planning Competition

The 2023 International Planning Competition

Learning Generalised Policies for Numeric Planning

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