@inproceedings{kimura-etal-2021-neuro,
title = "Neuro-Symbolic Reinforcement Learning with First-Order Logic",
author = "Kimura, Daiki and
Ono, Masaki and
Chaudhury, Subhajit and
Kohita, Ryosuke and
Wachi, Akifumi and
Agravante, Don Joven and
Tatsubori, Michiaki and
Munawar, Asim and
Gray, Alexander",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.283",
doi = "10.18653/v1/2021.emnlp-main.283",
pages = "3505--3511",
abstract = "Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro-symbolic framework called Logical Neural Network, which can learn symbolic and interpretable rules in their differentiable network. The method is first to extract first-order logical facts from text observation and external word meaning network (ConceptNet), then train a policy in the network with directly interpretable logical operators. Our experimental results show RL training with the proposed method converges significantly faster than other state-of-the-art neuro-symbolic methods in a TextWorld benchmark.",
}
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<abstract>Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro-symbolic framework called Logical Neural Network, which can learn symbolic and interpretable rules in their differentiable network. The method is first to extract first-order logical facts from text observation and external word meaning network (ConceptNet), then train a policy in the network with directly interpretable logical operators. Our experimental results show RL training with the proposed method converges significantly faster than other state-of-the-art neuro-symbolic methods in a TextWorld benchmark.</abstract>
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%0 Conference Proceedings
%T Neuro-Symbolic Reinforcement Learning with First-Order Logic
%A Kimura, Daiki
%A Ono, Masaki
%A Chaudhury, Subhajit
%A Kohita, Ryosuke
%A Wachi, Akifumi
%A Agravante, Don Joven
%A Tatsubori, Michiaki
%A Munawar, Asim
%A Gray, Alexander
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F kimura-etal-2021-neuro
%X Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro-symbolic framework called Logical Neural Network, which can learn symbolic and interpretable rules in their differentiable network. The method is first to extract first-order logical facts from text observation and external word meaning network (ConceptNet), then train a policy in the network with directly interpretable logical operators. Our experimental results show RL training with the proposed method converges significantly faster than other state-of-the-art neuro-symbolic methods in a TextWorld benchmark.
%R 10.18653/v1/2021.emnlp-main.283
%U https://aclanthology.org/2021.emnlp-main.283
%U https://doi.org/10.18653/v1/2021.emnlp-main.283
%P 3505-3511
Markdown (Informal)
[Neuro-Symbolic Reinforcement Learning with First-Order Logic](https://aclanthology.org/2021.emnlp-main.283) (Kimura et al., EMNLP 2021)
ACL
- Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, and Alexander Gray. 2021. Neuro-Symbolic Reinforcement Learning with First-Order Logic. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3505–3511, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
