We develop a probabilistic graphical model (PGM) for artificially intelligent (AI) agents to infer human beliefs during a simulated urban search and rescue (USAR) scenario executed in a Minecraft environment with a team of three players. The PGM approach makes observable states and actions explicit, as well as beliefs and intentions grounded by evidence about what players see and do over time. This approach also supports inferring the effect of interventions, which are vital if AI agents are to assist human teams. The experiment incorporates manipulations of players' knowledge, and the virtual Minecraft-based testbed provides access to several streams of information, including the objects in the players' field of view. The participants are equipped with a set of marker blocks that can be placed near room entrances to signal the presence or absence of victims in the rooms to their teammates. In each team, one of the members is given a different legend for the markers than the other two, which may mislead them about the state of the rooms; that is, they will hold a false belief. We extend previous works in this field by introducing ToMCAT, an AI agent that can reason about individual and shared mental states. We find that the players' behaviors are affected by what they see in their in-game field of view, their beliefs about the meaning of the markers, and their beliefs about which meaning the team decided to adopt. In addition, we show that ToMCAT's beliefs are consistent with the players' actions and that it can infer false beliefs with accuracy significantly better than chance and comparable to inferences made by human observers.

翻译：我们开发了一种概率图模型（PGM），用于人工智能（AI）代理在模拟城市搜救（USAR）场景中推断人类信念，该场景在Minecraft环境中由三名玩家组成的团队执行。PGM方法使可观测状态和行动显式化，同时通过随时间推移玩家所见所为的证据，使信念和意图有据可依。该方法还支持推断干预措施的效果，这对于AI代理协助人类团队至关重要。实验对玩家的知识进行了操纵，基于虚拟Minecraft的测试平台可提供多种信息流，包括玩家视野中的物体。参与者配备了一组标记块，可放置在房间入口附近，向队友传递房间内是否有受害者的信号。在每个团队中，其中一名成员获得的标记图例与其他两名成员不同，这可能导致其对房间状态产生误解；即他们将持有错误信念。我们通过引入ToMCAT（一种能够推理个体与共享心智状态的AI代理）扩展了该领域的先前工作。我们发现，玩家的行为受其在游戏视野中所见内容、对标记含义的信念以及他们认为团队采用哪种含义的信念影响。此外，我们证明ToMCAT的信念与玩家的行动一致，且其推断错误信念的准确率显著高于随机水平，并与人类观察者的推断相当。