Machine learning of Theory of Mind (ToM) is essential to build social agents
that co-live with humans and other agents. This capacity, once acquired, will
help machines infer the mental states of others from observed contextual action
trajectories, enabling future prediction of goals, intention, actions and
successor representations. The underlying mechanism for such a prediction
remains unclear, however. Inspired by the observation that humans often infer
the character traits of others, then use it to explain behaviour, we propose a
new neural ToM architecture that learns to generate a latent trait vector of an
actor from the past trajectories. This trait vector then multiplicatively
modulates the prediction mechanism via a `fast weights' scheme in the
prediction neural network, which reads the current context and predicts the
behaviour. We empirically show that the fast weights provide a good inductive
bias to model the character traits of agents and hence improves mindreading
ability. On the indirect assessment of false-belief understanding, the new ToM
model enables more efficient helping behaviours.

本文提出了一种新的神经 ToM 架构，它通过学习过去轨迹的潜在特征向量，来乘性调节预测机制，以提高情感理解能力，并在测试中取得了良好的效果。