A Planning Compilation to Reason about Goal Achievement at Planning Time
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916890874478592 |
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| author | Pozanco, Alberto Morales, Marianela Borrajo, Daniel Veloso, Manuela |
| author_facet | Pozanco, Alberto Morales, Marianela Borrajo, Daniel Veloso, Manuela |
| contents | Identifying the specific actions that achieve goals when solving a planning task might be beneficial for various planning applications. Traditionally, this identification occurs post-search, as some actions may temporarily achieve goals that are later undone and re-achieved by other actions. In this paper, we propose a compilation that extends the original planning task with commit actions that enforce the persistence of specific goals once achieved, allowing planners to identify permanent goal achievement during planning. Experimental results indicate that solving the reformulated tasks does not incur on any additional overhead both when performing optimal and suboptimal planning, while providing useful information for some downstream tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09545 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Planning Compilation to Reason about Goal Achievement at Planning Time Pozanco, Alberto Morales, Marianela Borrajo, Daniel Veloso, Manuela Artificial Intelligence Identifying the specific actions that achieve goals when solving a planning task might be beneficial for various planning applications. Traditionally, this identification occurs post-search, as some actions may temporarily achieve goals that are later undone and re-achieved by other actions. In this paper, we propose a compilation that extends the original planning task with commit actions that enforce the persistence of specific goals once achieved, allowing planners to identify permanent goal achievement during planning. Experimental results indicate that solving the reformulated tasks does not incur on any additional overhead both when performing optimal and suboptimal planning, while providing useful information for some downstream tasks. |
| title | A Planning Compilation to Reason about Goal Achievement at Planning Time |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.09545 |