用分支定界法高效识别多智能体团队与目标，行为推断更省时。
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
- 设计条件策略+因子化分支定界，联合推断团队与目标
- 在区块世界基准上达到穷举搜索的准确率，推理时间大幅减少
- 适合无人机监控、协作机器人等需行为推断的场景
多智能体目标识别要求观察者联合推断哪些智能体组成团队以及每个团队的目标，导致假设空间随团队划分和每队目标数呈组合爆炸。真实应用如无人机监视和协作机器人仅能观测智能体轨迹，迫使观察者仅凭行为对团队-目标假设进行排序。本文提出的MAGR-BB方法采用共享的团队与目标条件策略作为评分模型，嵌入因子化分支定界搜索中。在受控的多智能体区块世界基准上，MAGR-BB在整个轨迹过程中与穷举搜索返回相同的最优假设，同时将假设生成量级减少，并显著降低累积识别耗时。
原文摘要 · Abstract (English)
Multi-agent goal recognition asks an observer to jointly infer which agents act together and what each team is trying to achieve, so the hypothesis space grows combinatorially with the number of team partitions and goals per team. Real applications such as drone surveillance and collaborative robotics expose only the agents' trajectory, which forces the observer to rank team-goal hypotheses from behavior alone. Multi-Agent Goal Recognition with Branch-and-Bound (MAGR-BB) addresses this setting with a shared team- and goal-conditioned policy used as the scoring model inside a factorized branch-and-bound search. On a controlled multi-agent Blocksworld benchmark, MAGR-BB returns the same top-ranked hypothesis as exhaustive search throughout the trajectory while cutting hypothesis materialization by orders of magnitude and reducing cumulative recognition runtime substantially.
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