Extending Hierarchical Partial-Order Causal-Link Planning to Temporal Problem Solving

This paper introduces TEP (Temporal Event Planning), a novel POCL (Partial Ordered Causal Link) HTN approach tailored for temporal Hierarchical Task Networks (HTNs). TEP stands out for its capability to address all three categories of temporal problems, as categorized by Cushing, distinguishing it from prevailing approaches that primarily tackle the initial two categories. TEP accomplishes this by translating temporal HTN problems into non-temporal counterparts and excels in performance by leveraging heuristics developed in non-temporal HTN planning to steer the search for temporal solutions. This innovative strategy initiates by refining tasks into instantaneous actions and relaxing duration constraints, ensuring alignment with conventional search heuristics while upholding the problem's innate expressiveness.

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Published
2024-10-28
DOI
https://doi.org/10.1109/ictai62512.2024.00019
Primary Topic
AI-based Problem Solving and Planning
Type
article
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article

Extending Hierarchical Partial-Order Causal-Link Planning to Temporal Problem Solving

Humbert Fiorino, Damien Pellier, Nicolas Cavrel
AI-based Problem Solving and Planning
article

Extending Hierarchical Partial-Order Causal-Link Planning to Temporal Problem Solving

Humbert Fiorino, Damien Pellier, Nicolas Cavrel
article en

Abstract

This paper introduces TEP (Temporal Event Planning), a novel POCL (Partial Ordered Causal Link) HTN approach tailored for temporal Hierarchical Task Networks (HTNs). TEP stands out for its capability to address all three categories of temporal problems, as categorized by Cushing, distinguishing it from prevailing approaches that primarily tackle the initial two categories. TEP accomplishes this by translating temporal HTN problems into non-temporal counterparts and excels in performance by leveraging heuristics developed in non-temporal HTN planning to steer the search for temporal solutions. This innovative strategy initiates by refining tasks into instantaneous actions and relaxing duration constraints, ensuring alignment with conventional search heuristics while upholding the problem's innate expressiveness.

Laboratoire d'Informatique de Grenoble (FR), Université Grenoble Alpes (FR)
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AI-based Problem Solving and Planning
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