# Estimation of User's World Model Using Graph2vec

arXiv: https://arxiv.org/abs/2301.03793 · HTML: https://mlsanity.com/paper/2301.03793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 10 Jan 2023.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 2 of 2)

- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [World Model as a Graph: Learning Latent Landmarks for Planning](https://mlsanity.com/paper/2011.12491/) (2020)

## Abstract (arXiv)

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.