Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.21846 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911102068064256 |
|---|---|
| author | Zhang, Chenyuan Cardenas, Cristian Rojas Rezatofighi, Hamid Vered, Mor Say, Buser |
| author_facet | Zhang, Chenyuan Cardenas, Cristian Rojas Rezatofighi, Hamid Vered, Mor Say, Buser |
| contents | 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 Goal Recognition (AGR) focuses on strategically gathering information to reduce uncertainty. We adopt a probabilistic framework for Active Goal Recognition and propose an integrated solution that combines a joint belief update mechanism with a Monte Carlo Tree Search (MCTS) algorithm, allowing the observer to plan efficiently and infer the actor's hidden goal without requiring domain-specific knowledge. Through comprehensive empirical evaluation in a grid-based domain, we show that our joint belief update significantly outperforms passive goal recognition, and that our domain-independent MCTS performs comparably to our strong domain-specific greedy baseline. These results establish our solution as a practical and robust framework for goal inference, advancing the field toward more interactive and adaptive multi-agent systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21846 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic Active Goal Recognition Zhang, Chenyuan Cardenas, Cristian Rojas Rezatofighi, Hamid Vered, Mor Say, Buser Artificial Intelligence Symbolic Computation 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 Goal Recognition (AGR) focuses on strategically gathering information to reduce uncertainty. We adopt a probabilistic framework for Active Goal Recognition and propose an integrated solution that combines a joint belief update mechanism with a Monte Carlo Tree Search (MCTS) algorithm, allowing the observer to plan efficiently and infer the actor's hidden goal without requiring domain-specific knowledge. Through comprehensive empirical evaluation in a grid-based domain, we show that our joint belief update significantly outperforms passive goal recognition, and that our domain-independent MCTS performs comparably to our strong domain-specific greedy baseline. These results establish our solution as a practical and robust framework for goal inference, advancing the field toward more interactive and adaptive multi-agent systems. |
| title | Probabilistic Active Goal Recognition |
| topic | Artificial Intelligence Symbolic Computation |
| url | https://arxiv.org/abs/2507.21846 |