Probabilistic Modeling of Human Teams to Infer False Beliefs
Paulo Soares Adarsh Pyarelal Kobus Barnard
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.