Theory of Mind (ToM) is the ability to attribute mental states to others, the
basis of human cognition. At present, there has been growing interest in the AI
with cognitive abilities, for example in healthcare and the motoring industry.
Beliefs, desires, and intentions are the early abilities of infants and the
foundation of human cognitive ability, as well as for machine with ToM. In this
paper, we review recent progress in machine ToM on beliefs, desires, and
intentions. And we shall introduce the experiments, datasets and methods of
machine ToM on these three aspects, summarize the development of different
tasks and datasets in recent years, and compare well-behaved models in aspects
of advantages, limitations and applicable conditions, hoping that this study
can guide researchers to quickly keep up with latest trend in this field.
Unlike other domains with a specific task and resolution framework, machine ToM
lacks a unified instruction and a series of standard evaluation tasks, which
make it difficult to formally compare the proposed models. We argue that, one
method to address this difficulty is now to present a standard assessment
criteria and dataset, better a large-scale dataset covered multiple aspects of
ToM.

本文综述了近年来关于机器 Theory of Mind（ToM）在信仰、欲望和意图方面的进展，概括了不同任务和数据集的发展，并比较了具有优势、局限性和适用条件的模型。我们认为，提出标准的评估标准和数据集，特别是覆盖了 ToM 多个方面的大规模数据集，是解决这种困难的方法之一。