让观察者主动探查以推断他人目标，提升多智能体交互效率。
Probabilistic Active Goal Recognition
- 用概率框架结合信念更新与蒙特卡洛树搜索，主动规划信息获取。
- 在网格环境中显著优于被动推理，且不依赖特定领域知识。
- 适合开发自适应多智能体系统，如机器人协作或AI对手设计。
在多智能体环境中，有效交互依赖于对其他智能体信念与意图的理解。以往的目标识别研究大多将观察者视为被动推理者，而主动目标识别（AGR）则通过策略性地收集信息来降低不确定性。本文采用概率框架，提出一种融合联合信念更新机制与蒙特卡洛树搜索（MCTS）的集成方案，使观察者能在无需领域知识的情况下高效规划并推断行动者的隐藏目标。在基于网格环境的全面实验中，我们证明联合信念更新显著优于被动目标识别，且所提出的领域无关MCTS性能接近强领域特定的贪心基线。结果表明，该方法为目标推断提供了一个实用且鲁棒的框架，推动多智能体系统向更交互、更自适应的方向发展。
原文摘要 · Abstract (English)
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.
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