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arXiv · 2603.14824

Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version

Giacomo Rosa·Jean Honorio·Nir Lipovetzky·Sebastian Sardina
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Abstract

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.

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Giacomo Rosa, Jean Honorio, Nir Lipovetzky, Sebastian Sardina. 2026-03-16. Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version. https://arxiv.org/abs/2603.14824

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