An Extensive Empirical Evaluation of Inferring Preconditions and Effects of Compound Tasks in Ground HTN Planning Problems

HTN planning requires the decomposition of compound tasks into primitive and executable actions. In the currently most frequently used formalism, compound tasks lack explicit preconditions and effects. Those are, however, useful, e.g., for pruning techniques, heuristics, or the comprehension of domains. Previously, we introduced and formalized different kinds of inferred preconditions and effects of compound tasks based on their decomposition methods together with a complexity analysis. In this paper, we present an empirical evaluation of computing these inferred preconditions and effects using the IPC benchmark sets. Specifically, we analyze their frequency of occurrence and compare the performance of an approximation to the exact preconditions and effects. Our goal is to provide a comprehensive overview of the proposed techniques, enabling researchers to determine the extent to which they can be utilized in their given application.

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Publication Details

Journal
Journal of Artificial Intelligence Research
Published
2025-03-15
DOI
https://doi.org/10.1613/jair.1.17279
Primary Topic
Satellite Communication Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

An Extensive Empirical Evaluation of Inferring Preconditions and Effects of Compound Tasks in Ground HTN Planning Problems

Pascal Bercher, Conny Olz, Mario Schmautz, Susanne Biundo et al.
Journal of Artificial Intelligence Research
Satellite Communication Systems
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An Extensive Empirical Evaluation of Inferring Preconditions and Effects of Compound Tasks in Ground HTN Planning Problems

Pascal Bercher, Conny Olz, Mario Schmautz, Susanne Biundo, Benedikt Jutz, Alexander Lodemann, Maximilian Borowiec
article en

Abstract

HTN planning requires the decomposition of compound tasks into primitive and executable actions. In the currently most frequently used formalism, compound tasks lack explicit preconditions and effects. Those are, however, useful, e.g., for pruning techniques, heuristics, or the comprehension of domains. Previously, we introduced and formalized different kinds of inferred preconditions and effects of compound tasks based on their decomposition methods together with a complexity analysis. In this paper, we present an empirical evaluation of computing these inferred preconditions and effects using the IPC benchmark sets. Specifically, we analyze their frequency of occurrence and compare the performance of an approximation to the exact preconditions and effects. Our goal is to provide a comprehensive overview of the proposed techniques, enabling researchers to determine the extent to which they can be utilized in their given application.

Journal of Artificial Intelligence ResearchVol. 82
Australian National University (AU), Universität Ulm (DE)
Openalex Percentile: Top 94%
Satellite Communication Systems
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