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。

通过动态特质归因学习心智理论

Learning Theory of Mind via Dynamic Traits Attribution

In this paper we present the first work on the automated scoring of
mindreading ability in middle childhood and early adolescence. We create
MIND-CA, a new corpus of 11,311 question-answer pairs in English from 1,066
children aged 7 to 14. We perform machine learning experiments and carry out
extensive quantitative and qualitative evaluation. We obtain promising results,
demonstrating the applicability of state-of-the-art NLP solutions to a new
domain and task.

本文的第一项工作是自动评分中儿童和早期青春期的心智阅读能力。通过创建 MIND-CA 的新语料库，作者对 1,066 名 7 至 14 岁儿童创建了 11,311 个问题 - 回答对。作者进行了机器学习实验并进行了广泛的定量和定性评估。结果很有前途，证明了最先进的 NLP 解决方案对于新的领域和任务具有适用性。