  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.
