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Oral
Machine Theory of Mind
Neil Rabinowitz · Frank Perbet · Francis Song · Chiyuan Zhang · S. M. Ali Eslami · Matthew Botvinick

Wed Jul 11 08:00 AM -- 08:20 AM (PDT) @ A3
in Reinforcement Learning 7 »

Theory of mind (ToM) broadly refers to humans' ability to represent the mental states of others, including their desires, beliefs, and intentions. We design a Theory of Mind neural network – a ToMnet – which uses meta-learning to build such models of the agents it encounters. The ToMnet learns a strong prior model for agents’ future behaviour, and, using only a small number of behavioural observations, can bootstrap to richer predictions about agents’ characteristics and mental states. We apply the ToMnet to agents behaving in simple gridworld environments, showing that it learns to model random, algorithmic, and deep RL agents from varied populations, and that it passes classic ToM tasks such as the "Sally-Anne" test of recognising that others can hold false beliefs about the world.

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Author Information

Neil Rabinowitz (DeepMind)
Frank Perbet (DeepMind)
Francis Song (DeepMind)
Chiyuan Zhang (Google)
S. M. Ali Eslami (DeepMind)
S. M. Ali Eslami

S. M. Ali Eslami is a staff research scientist at DeepMind working on problems related to artificial intelligence. Prior to that, he was a post-doctoral researcher at Microsoft Research in Cambridge. He did his PhD in the School of Informatics at the University of Edinburgh, during which he was also a visiting researcher in the Visual Geometry Group at the University of Oxford. His research is focused on figuring out how we can get computers to learn with less human supervision.

Matthew Botvinick (DeepMind)

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