Estimation of User's World Model Using Graph2vec

TL;DR

This paper introduces a novel approach for robots to estimate a user's world model by leveraging graph2vec to learn distributed representations, enabling more efficient understanding of user queries in autonomous interactions.

Contribution

The paper proposes a new method combining graph2vec and concept activation vectors to estimate user world models from queries, improving efficiency over simple search methods.

Findings

The method effectively estimates user world models.

It outperforms simple query search approaches.

Experimental results demonstrate improved efficiency.

Abstract

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.