Machine Theory of Mind

TL;DR

This paper introduces a neural network model called ToMnet that uses meta-learning to develop Theory of Mind capabilities in machines, enabling them to model and predict other agents' mental states from limited observations.

Contribution

The paper presents the design and application of ToMnet, a novel neural network that learns to model various agents' behaviors and mental states, including passing classic ToM tests.

Findings

ToMnet successfully models different types of agents in gridworld environments.

It passes the 'Sally-Anne' false belief test.

The system demonstrates strong predictive capabilities with minimal observations.

Abstract

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.