Subgoaling Relaxation-based Heuristics for Numeric Planning with Infinite Actions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912791006281728 |
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| author | Aso-Mollar, Ángel Aineto, Diego Scala, Enrico Onaindia, Eva |
| author_facet | Aso-Mollar, Ángel Aineto, Diego Scala, Enrico Onaindia, Eva |
| contents | Numeric planning with control parameters extends the standard numeric planning model by introducing action parameters as free numeric variables that must be instantiated during planning. This results in a potentially infinite number of applicable actions in a state. In this setting, off-the-shelf numeric heuristics that leverage the action structure are not feasible. In this paper, we identify a tractable subset of these problems--namely, controllable, simple numeric problems--and propose an optimistic compilation approach that transforms them into simple numeric tasks. To do so, we abstract control-dependent expressions into bounded constant effects and relaxed preconditions. The proposed compilation makes it possible to effectively use subgoaling heuristics to estimate goal distance in numeric planning problems involving control parameters. Our results demonstrate that this approach is an effective and computationally feasible way of applying traditional numeric heuristics to settings with an infinite number of possible actions, pushing the boundaries of the current state of the art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Subgoaling Relaxation-based Heuristics for Numeric Planning with Infinite Actions Aso-Mollar, Ángel Aineto, Diego Scala, Enrico Onaindia, Eva Artificial Intelligence Numeric planning with control parameters extends the standard numeric planning model by introducing action parameters as free numeric variables that must be instantiated during planning. This results in a potentially infinite number of applicable actions in a state. In this setting, off-the-shelf numeric heuristics that leverage the action structure are not feasible. In this paper, we identify a tractable subset of these problems--namely, controllable, simple numeric problems--and propose an optimistic compilation approach that transforms them into simple numeric tasks. To do so, we abstract control-dependent expressions into bounded constant effects and relaxed preconditions. The proposed compilation makes it possible to effectively use subgoaling heuristics to estimate goal distance in numeric planning problems involving control parameters. Our results demonstrate that this approach is an effective and computationally feasible way of applying traditional numeric heuristics to settings with an infinite number of possible actions, pushing the boundaries of the current state of the art. |
| title | Subgoaling Relaxation-based Heuristics for Numeric Planning with Infinite Actions |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.22367 |