A Probabilistic Framework for Hierarchical Goal Recognition

arXiv cs.AI / 4/27/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper addresses goal recognition by inferring an agent’s goals from behavioral observations while leveraging hierarchical task structure and uncertainty-aware reasoning.
It presents the first planning-based probabilistic framework for hierarchical goal recognition using Hierarchical Task Networks (HTNs), combining HTN planning structure with probabilistic inference.
The approach instantiates a likelihood estimator with a three-stage generative model within an HTN planner to compute posterior distributions over goal hypotheses.
Experiments on HTN benchmarks show improved recognition performance compared with an existing HTN-based recognizer.
The authors position the framework as a foundation for more practical, probabilistic goal recognition grounded in hierarchical planning.

Abstract

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.