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.

翻译：目标识别旨在根据对主体行为的观测推断其目标。在现实环境中，利用层次化任务结构并在不确定性下进行推理，能够提升识别效果。基于规划的目标识别在过去十年中取得了显著进展，但据我们所知，现有方法尚未将层次化任务结构与概率推断进行联合集成。本文提出了首个基于规划的层次化目标识别概率框架，该框架基于层次化任务网络（HTNs）实现。我们通过采用具有三阶段生成模型的HTN规划器进行似然估计，从而实例化该框架，得到目标假设的后验分布。实验结果表明，在HTN基准测试中，该框架相比现有基于HTN的识别器具有更优的识别性能。总体而言，本框架为基于层次化规划结构的概率目标识别奠定了基础，推动目标识别向更实用的场景发展。