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Estimation of User's World Model Using Graph2vec

2023-01-10 05:37:11
Tatsuya Sakai, Takayuki Nagai
arXiv_RO
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2301.03793

PDF

https://arxiv.org/pdf/2301.03793.pdf

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