{"ID":2889231,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21846","arxiv_id":"2507.21846","title":"Probabilistic Active Goal Recognition","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.21846","url_pdf":"https://arxiv.org/pdf/2507.21846v2","authors":"[\"Chenyuan Zhang\",\"Cristian Rojas Cardenas\",\"Hamid Rezatofighi\",\"Mor Vered\",\"Buser Say\"]","published":"2025-07-29T14:22:29Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.SC\"]","methods":"[]","has_code":false}