Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918405586550784 |
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| author | Rosa, Giacomo Honorio, Jean Lipovetzky, Nir Sardina, Sebastian |
| author_facet | Rosa, Giacomo Honorio, Jean Lipovetzky, Nir Sardina, Sebastian |
| contents | Classical planning aims to find a sequence of actions, a plan, that maps a starting state into one of the goal states. If a trajectory appears to be leading to the goal, should we prioritise exploring it? Seminal work in goal recognition (GR) has defined GR in terms of a classical planning problem, adopting classical solvers and heuristics to recognise plans. We come full circle, and study the adoption and properties of GR-derived heuristics for seeking solutions to classical planning problems. We propose a new divergence-based framework for assessing goal intention, which informs a new class of efficiently-computable heuristics. As a proof of concept, we derive two such heuristics, and show that they can already yield improvements for top-scoring classical planners. Our work provides foundational knowledge for understanding and deriving probabilistic intention-based heuristics for planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14824 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version Rosa, Giacomo Honorio, Jean Lipovetzky, Nir Sardina, Sebastian Artificial Intelligence Classical planning aims to find a sequence of actions, a plan, that maps a starting state into one of the goal states. If a trajectory appears to be leading to the goal, should we prioritise exploring it? Seminal work in goal recognition (GR) has defined GR in terms of a classical planning problem, adopting classical solvers and heuristics to recognise plans. We come full circle, and study the adoption and properties of GR-derived heuristics for seeking solutions to classical planning problems. We propose a new divergence-based framework for assessing goal intention, which informs a new class of efficiently-computable heuristics. As a proof of concept, we derive two such heuristics, and show that they can already yield improvements for top-scoring classical planners. Our work provides foundational knowledge for understanding and deriving probabilistic intention-based heuristics for planning. |
| title | Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.14824 |