13 citations · 50 across the 16 of their papers we have counts for
16 papers
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning
Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura +8
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do n…
Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis
Yeldar Toleubay, Don Joven Agravante, Daiki Kimura +3
In response to the global challenge of mental health problems, we proposes a Logical Neural Network (LNN) based Neuro-Symbolic AI method for the diagnosis of mental disorders. Due…
DiffG-RL: Leveraging Difference between State and Common Sense
Tsunehiko Tanaka, Daiki Kimura, Michiaki Tatsubori
Taking into account background knowledge as the context has always been an important part of solving tasks that involve natural language. One representative example of such tasks i…
Commonsense Knowledge from Scene Graphs for Textual Environments
Tsunehiko Tanaka, Daiki Kimura, Michiaki Tatsubori
Text-based games are becoming commonly used in reinforcement learning as real-world simulation environments. They are usually imperfect information games, and their interactions ar…
LOA: Logical Optimal Actions for Text-based Interaction Games
Daiki Kimura, Subhajit Chaudhury, Masaki Ono +6
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 ne…
Neuro-Symbolic Reinforcement Learning with First-Order Logic
Daiki Kimura, Masaki Ono, Subhajit Chaudhury +6
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 conv…