1. LOA: Logical Optimal Actions for Text-based Interaction Games
- Author
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Kimura, Daiki, Chaudhury, Subhajit, Ono, Masaki, Tatsubori, Michiaki, Agravante, Don Joven, Munawar, Asim, Wachi, Akifumi, Kohita, Ryosuke, and Gray, Alexander
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Machine Learning ,Computer Science - Robotics - 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. Code: https://github.com/ibm/loa, Comment: ACL-IJCNLP 2021 (demo paper)
- Published
- 2021