  Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans'
ability to represent the mental states of others, including their desires,
beliefs, and intentions. We propose to train a machine to build such models
too. We design a Theory of Mind neural network -- a ToMnet -- which uses
meta-learning to build models of the agents it encounters, from observations of
their behaviour alone. Through this process, it acquires a strong prior model
for agents' behaviour, as well as the ability to bootstrap to richer
predictions about agents' characteristics and mental states using only a small
number of behavioural observations. We apply the ToMnet to agents behaving in
simple gridworld environments, showing that it learns to model random,
algorithmic, and deep reinforcement learning agents from varied populations,
and that it passes classic ToM tasks such as the "Sally-Anne" test (Wimmer &
Perner, 1983; Baron-Cohen et al., 1985) of recognising that others can hold
false beliefs about the world. We argue that this system -- which autonomously
learns how to model other agents in its world -- is an important step forward
for developing multi-agent AI systems, for building intermediating technology
for machine-human interaction, and for advancing the progress on interpretable
AI.
