A Probabilistic Framework for Hierarchical Goal Recognition
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, yie...
| Authors | Chenyuan Zhang, Katherine Ip, Hamid Rezatofighi et al. |
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| Primary category | cs.SC |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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