A Probabilistic Framework for Hierarchical Goal Recognition

TL;DR

This paper introduces a novel probabilistic framework for hierarchical goal recognition using Hierarchical Task Networks, improving recognition performance by integrating hierarchical planning with probabilistic inference.

Contribution

It is the first to combine hierarchical task structure with probabilistic inference in a planning-based goal recognition framework.

Findings

Improved recognition accuracy over existing HTN-based recognizers.

Utilizes a three-stage generative model for likelihood estimation.

Provides a foundation for practical hierarchical goal recognition.

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.