[2603.14824] Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version

Computer Science > Artificial Intelligence arXiv:2603.14824 (cs) [Submitted on 16 Mar 2026 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version Authors:Giacomo Rosa, Jean Honorio, Nir Lipovetzky, Sebastian Sardina View a PDF of the paper titled Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version, by Giacomo Rosa and 3 other authors View PDF HTML (experimental) 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. Comments: Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.14824 [cs.AI...