A Probabilistic Framework for Hierarchical Goal Recognition

2026-04-24 · Chenyuan Zhang, Katherine Ip, Hamid Rezatofighi, Buser Say, Mor Vered arxiv

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.

📄 PDF Abstract BibTeX arXiv:2604.22256

Code (0)

등록된 구현이 없습니다.

Probabilistic Active Goal Recognition

2025-07-29 · Chenyuan Zhang, Cristian Rojas Cardenas, Hamid Rezatofighi, Mor Vered 외 arxiv

In multi-agent environments, effective interaction hinges on understanding the beliefs and intentions of other agents. While prior work on goal recognition has largely treated the observer as a passive reasoner, Active G…

Inferring Agents Preferences as Priors for Probabilistic Goal Recognition

2021-02-23 · Kin Max Gusmão, Ramon Fraga Pereira, Felipe Meneguzzi

Recent approaches to goal recognition have leveraged planning landmarks to achieve high-accuracy with low runtime cost. These approaches, however, lack a probabilistic interpretation. Furthermore, while most probabilisti…

Probabilistic Subgoal Representations for Hierarchical Reinforcement learning

2024-06-24 · Vivienne Huiling Wang, Tinghuai Wang, Wenyan Yang, Joni-Kristian Kämäräinen 외

In goal-conditioned hierarchical reinforcement learning (HRL), a high-level policy specifies a subgoal for the low-level policy to reach. Effective HRL hinges on a suitable subgoal represen tation function, abstracting s…

Gaussian Processes Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning

Improved Inference of Human Intent by Combining Plan Recognition and Language Feedback

2023-10-03 · Ifrah Idrees, Tian Yun, Naveen Sharma, Yunxin Deng 외

Conversational assistive robots can aid people, especially those with cognitive impairments, to accomplish various tasks such as cooking meals, performing exercises, or operating machines. However, to interact with peopl…

When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting

2022-06-16 · Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang 외

Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most me…

Time Series Time Series Analysis Time Series Forecasting