# A Probabilistic Framework for Hierarchical Goal Recognition

arXiv: https://arxiv.org/abs/2604.22256 · HTML: https://mlsanity.com/paper/2604.22256/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 4 of 4)

- [Exploring the hierarchical structure of human plans via program generation](https://mlsanity.com/paper/2311.18644/) (2023)
- [Heuristic Online Goal Recognition in Continuous Domains](https://mlsanity.com/paper/1709.09839/) (2017)
- [SLIM: Semi-Lazy Inference Mechanism for Plan Recognition](https://mlsanity.com/paper/1703.00838/) (2017)
- [Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability](https://mlsanity.com/paper/2402.10510/) (2024)

## Abstract (arXiv)

Goal recognition aims to infer an agent's goal from observations of its behaviour. In realistic settings, recognition can benefit from exploiting hierarchical task structure and reasoning under uncertainty. Planning-based goal recognition has made substantial progress over the past decade, but to the best of our knowledge no existing approach jointly integrates hierarchical task structure with probabilistic inference. In this paper, we introduce the first planning-based probabilistic framework for hierarchical goal recognition over Hierarchical Task Networks (HTNs). We instantiate the framework by exploiting an HTN planner with a three-stage generative model for likelihood estimation, yielding posterior distributions over goal hypotheses. Empirical results show improved recognition performance over the existing HTN-based recognizer on HTN benchmarks. Overall, the framework lays a foundation for probabilistic goal recognition grounded in hierarchical planning structure, moving goal recognition toward more practical settings.