首次将概率推理与层次任务结构结合，提升智能体目标识别准确率。
A Probabilistic Framework for Hierarchical Goal Recognition
- 基于HTN规划器构建三阶段生成模型估算似然
- 在HTN基准测试中识别性能优于现有方法
- 适合需处理不确定性和复杂任务结构的应用场景
目标识别旨在通过观察智能体行为推断其目标。在真实场景中，利用层次任务结构并进行不确定性推理可显著提升识别效果。尽管基于规划的目标识别在过去十年取得长足进展，但据我们所知，尚无方法能同时整合层次任务结构与概率推理。本文提出首个基于规划的、面向层次任务网络（HTNs）的概率目标识别框架。通过结合HTN规划器与三阶段生成模型估算似然，得到目标假设的后验分布。实验结果表明，在HTN基准测试上，该框架的识别性能优于现有基于HTN的方法。整体而言，该框架为基于层次规划结构的概率目标识别奠定基础，推动目标识别向更实用场景迈进。
原文摘要 · Abstract (English)
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.
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