13 citations · 29 across the 16 of their papers we have counts for
16 papers
GenAIOps for GenAI Model-Agility
Ken Ueno, Makoto Kogo, Hiromi Kawatsu +2
AI-agility, with which an organization can be quickly adapted to its business priorities, is desired even for the development and operations of generative AI (GenAI) applications.…
Matching of Descriptive Labels to Glossary Descriptions
Toshihiro Takahashi, Takaaki Tateishi, Michiaki Tatsubori
Semantic text similarity plays an important role in software engineering tasks in which engineers are requested to clarify the semantics of descriptive labels (e.g., business terms…
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…
Deep Temporal Interpolation of Radar-based Precipitation
Michiaki Tatsubori, Takao Moriyama, Tatsuya Ishikawa +5
When providing the boundary conditions for hydrological flood models and estimating the associated risk, interpolating precipitation at very high temporal resolutions (e.g. 5 minut…
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…
Online Adaptation of Parameters using GRU-based Neural Network with BO for Accurate Driving Model
Zhanhong Yang, Satoshi Masuda, Michiaki Tatsubori
Testing self-driving cars in different areas requires surrounding cars with accordingly different driving styles such as aggressive or conservative styles. A method of numerically…
Reinforcement Learning with External Knowledge by using Logical Neural Networks
Daiki Kimura, Subhajit Chaudhury, Akifumi Wachi +4
Conventional deep reinforcement learning methods are sample-inefficient and usually require a large number of training trials before convergence. Since such methods operate on an u…
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning
Thomas Carta, Subhajit Chaudhury, Kartik Talamadupula +1
We present VisualHints, a novel environment for multimodal reinforcement learning (RL) involving text-based interactions along with visual hints (obtained from the environment). Re…
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games
Subhajit Chaudhury, Daiki Kimura, Kartik Talamadupula +3
We show that Reinforcement Learning (RL) methods for solving Text-Based Games (TBGs) often fail to generalize on unseen games, especially in small data regimes. To address this iss…
Design and Implementation of Linked Planning Domain Definition Language
Michiaki Tatsubori, Asim Munawar, Takao Moriyama
Planning is a critical component of any artificial intelligence system that concerns the realization of strategies or action sequences typically for intelligent agents and autonomo…
Reinforcement Learning Testbed for Power-Consumption Optimization
Takao Moriyama, Giovanni De Magistris, Michiaki Tatsubori +3
Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and…
MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning
Asim Munawar, Giovanni De Magistris, Tu-Hoa Pham +5
This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural lan…