@inproceedings{kimura-etal-2021-loa,
title = "{LOA}: Logical Optimal Actions for Text-based Interaction Games",
author = "Kimura, Daiki and
Chaudhury, Subhajit and
Ono, Masaki and
Tatsubori, Michiaki and
Agravante, Don Joven and
Munawar, Asim and
Wachi, Akifumi and
Kohita, Ryosuke and
Gray, Alexander",
editor = "Ji, Heng and
Park, Jong C. and
Xia, Rui",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-demo.27",
doi = "10.18653/v1/2021.acl-demo.27",
pages = "227--231",
abstract = "We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Demo site: \url{https://ibm.biz/acl21-loa}, Code: \url{https://github.com/ibm/loa}",
}
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%0 Conference Proceedings
%T LOA: Logical Optimal Actions for Text-based Interaction Games
%A Kimura, Daiki
%A Chaudhury, Subhajit
%A Ono, Masaki
%A Tatsubori, Michiaki
%A Agravante, Don Joven
%A Munawar, Asim
%A Wachi, Akifumi
%A Kohita, Ryosuke
%A Gray, Alexander
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F kimura-etal-2021-loa
%X We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Demo site: https://ibm.biz/acl21-loa, Code: https://github.com/ibm/loa
%R 10.18653/v1/2021.acl-demo.27
%U https://aclanthology.org/2021.acl-demo.27
%U https://doi.org/10.18653/v1/2021.acl-demo.27
%P 227-231
Markdown (Informal)
[LOA: Logical Optimal Actions for Text-based Interaction Games](https://aclanthology.org/2021.acl-demo.27) (Kimura et al., ACL-IJCNLP 2021)
ACL
- Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, and Alexander Gray. 2021. LOA: Logical Optimal Actions for Text-based Interaction Games. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 227–231, Online. Association for Computational Linguistics.