Symbol emergence using Variational autoencoder
-
- YOSHIDA Yuto
- Ritsumeikan University
-
- HAGIWARA Yoshinobu
- Ritsumeikan University
-
- TANIGUCHI Akira
- Ritsumeikan University
-
- TANIGUCHI Tadahiro
- Ritsumeikan University
Bibliographic Information
- Other Title
-
- 変分オートエンコーダを活用した実画像からの記号創発
Abstract
<p>In this paper, we propose a computational model that realizes symbol emergence between two agents observing real images using Variational autoencoders(VAEs). When humans communicate information with others, they communicate using signs such as words and signals. This study is conducted to build computational models that reproduce the emergence of symbolic communication between humans to obtain better understanding. In this study, we used VAE to model the representation of symbol emergence between two agents that perform category formation from real images. Using the SERKET framework, the representation learning is effectively influenced by the symbol emergence at the social level. The results of experiments demonstrated that categories are formed from real images observed by agents, and signs are shared appropriately among agents through symbolic communication. In addition, the images recalled by the agents confirmed that the objects in the recalled images were shared among the agents.</p>
Journal
-
- Proceedings of the Annual Conference of JSAI
-
Proceedings of the Annual Conference of JSAI JSAI2022 (0), 3L3GS802-3L3GS802, 2022
The Japanese Society for Artificial Intelligence
- Tweet
Details 詳細情報について
-
- CRID
- 1390574181079187584
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
-
- Abstract License Flag
- Disallowed