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.

通过在 Minecraft 环境中模拟一个由三个玩家组成的团队，在人工智能代理商推理人类信念的过程中，我们开发了一种概率图模型（PGM）。我们的实验表明，玩家的行为受到游戏中视野内所见的影响，以及他们对标记意义的信念和团队决定采用哪个意义的信念。与人类观察者的推理相比，我们的 AI 代理商 ToMCAT 对于推断虚假信念具有显著优势。