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.

翻译：心智理论（ToM）是指将心理状态归因于他人的能力，是人类认知的基础。目前，人们对具备认知能力的人工智能日益感兴趣，例如在医疗和汽车工业领域。信念、欲望和意图是婴幼儿的早期能力，也是人类认知能力的基础，同样适用于具备心智理论的机器。本文综述了机器心智理论在信念、欲望和意图方面的最新进展。我们将介绍这三方面机器心智理论的实验、数据集和方法，总结近年来不同任务与数据集的发展，并在优势、局限性和适用条件等方面对比表现良好的模型，以期本研究能指导研究人员快速跟进该领域的最新趋势。与其他拥有特定任务和解决框架的领域不同，机器心智理论缺乏统一的指导标准和一系列标准评估任务，这使得难以对提出的模型进行正式比较。我们认为，解决这一难题的一种方法是现在提出一套标准评估标准和数据集，最好是一个涵盖心智理论多个方面的大规模数据集。