A Review on Machine Theory of Mind

Abstract

Theory of Mind (ToM) is the ability to attribute mental states to others, an important component of human cognition. At present, there has been growing interest in the artificial intelligence (AI) with cognitive abilities, for example in healthcare and the motoring industry. Research indicates that infants exhibit early signs in cognitive and social understanding, including some basic abilities related to beliefs, desires, and intentions (BDIs). Thus, the ability to attribute BDIs to others is also crucial for the development of machine ToM. In this article, we review recent progress in machine ToM on BDIs. 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. And the existing models still cannot exhibit the same ToM reasoning ability as real humans, lack of transferability, interpretability, few-shot learning, etc. 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. Besides, for developing an AI of ToM, it requires the cooperation of experts from various domains.