Representation Learning of World Models and Estimation of World Model of Others Using Graph2vec

Abstract

To realize advanced interaction between autonomous robots and users, it is important for robots to aware the difference in their state space representations (i.e., world models). As a first step toward this goal, we propose a method to estimate user's world model based on queries. In our method, the agent learns distributed representation of world models by graph2vec and generates concept activation vectors (CAVs) that represent the meaning of queries in latent space. The experimental results show that our method can estimate user's world model more efficiently than the simple method using ``AND'' search of queries.