To obtain advanced interaction between autonomous robots and users, robots should be able to distinguish their state space representations (i.e., world models). Herein, a novel method was proposed for estimating the user's world model based on queries. In this method, the agent learns the distributed representation of world models using graph2vec and generates concept activation vectors that represent the meaning of queries in the latent space. Experimental results revealed that the proposed method can estimate the user's world model more efficiently than the simple method of using the ``AND'' search of queries.

翻译：为了获得自主机器人和用户之间的先进互动,机器人应能区分其国家空间表现(即世界模型),因此,提出了一种根据查询估计用户世界模型的新颖方法,在这种方法中,代理人利用图示2vec学习世界模型分布的表示方式,并生成概念激活矢量,这些矢量代表潜在空间查询的含义。实验结果表明,拟议的方法可以比使用“AND”查询的简单方法更高效地估计用户的世界模型。