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

arXiv: https://arxiv.org/abs/2603.14824 · HTML: https://mlsanity.com/paper/2603.14824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 3 of 3)

- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Count-based Novelty Exploration in Classical Planning](https://mlsanity.com/paper/2408.13719/) (2024)
- [Fact Probability Vector Based Goal Recognition](https://mlsanity.com/paper/2408.14224/) (2024)

## Abstract (arXiv)

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.