Search Results for author: Michiaki Tatsubori

Found 16 papers, 6 papers with code

Neuro-Symbolic Approaches for Text-Based Policy Learning

1 code implementation EMNLP 2021 Subhajit Chaudhury, Prithviraj Sen, Masaki Ono, Daiki Kimura, Michiaki Tatsubori, Asim Munawar

We outline a method for end-to-end differentiable symbolic rule learning and show that such symbolic policies outperform previous state-of-the-art methods in text-based RL for the coin collector environment from 5-10x fewer training games.

Reinforcement Learning (RL) text-based games

Matching of Descriptive Labels to Glossary Descriptions

no code implementations27 Oct 2023 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, table column names) that are often consists of too short or too generic words and appears in their IT systems.

Descriptive STS +1

Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning

1 code implementation5 Jul 2023 Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura, Prithviraj Sen, Keerthiram Murugesan, Rosario Uceda-Sosa, Michiaki Tatsubori, Achille Fokoue, Pavan Kapanipathi, Asim Munawar, Alexander Gray

Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.

reinforcement-learning Representation Learning

Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis

no code implementations6 Jun 2023 Yeldar Toleubay, Don Joven Agravante, Daiki Kimura, Baihan Lin, Djallel Bouneffouf, Michiaki Tatsubori

The proposed system addresses the lack of explainability of current Neural Network models and provides a more trustworthy solution for mental disorder diagnosis.

DiffG-RL: Leveraging Difference between State and Common Sense

1 code implementation29 Nov 2022 Tsunehiko Tanaka, Daiki Kimura, Michiaki Tatsubori

We propose a novel agent, DiffG-RL, which constructs a Difference Graph that organizes the environment states and common sense by means of interactive objects with a dedicated graph encoder.

Common Sense Reasoning text-based games

Commonsense Knowledge from Scene Graphs for Textual Environments

no code implementations19 Oct 2022 Tsunehiko Tanaka, Daiki Kimura, Michiaki Tatsubori

They are usually imperfect information games, and their interactions are only in the textual modality.

Common Sense Reasoning text-based games

Deep Temporal Interpolation of Radar-based Precipitation

no code implementations1 Mar 2022 Michiaki Tatsubori, Takao Moriyama, Tatsuya Ishikawa, Paolo Fraccaro, Anne Jones, Blair Edwards, Julian Kuehnert, Sekou L. Remy

When providing the boundary conditions for hydrological flood models and estimating the associated risk, interpolating precipitation at very high temporal resolutions (e. g. 5 minutes) is essential not to miss the cause of flooding in local regions.

Optical Flow Estimation

LOA: Logical Optimal Actions for Text-based Interaction Games

1 code implementation ACL 2021 Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander Gray

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.

reinforcement-learning Reinforcement Learning (RL) +1

Online Adaptation of Parameters using GRU-based Neural Network with BO for Accurate Driving Model

no code implementations24 Sep 2021 Zhanhong Yang, Satoshi Masuda, Michiaki Tatsubori

The experiments also showed that we can calibrate a corresponding DM in a virtual testing environment with up to 26% more accuracy than with fixed calibration methods.

Bayesian Optimization Self-Driving Cars

Reinforcement Learning with External Knowledge by using Logical Neural Networks

no code implementations3 Mar 2021 Daiki Kimura, Subhajit Chaudhury, Akifumi Wachi, Ryosuke Kohita, Asim Munawar, Michiaki Tatsubori, Alexander Gray

Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.

reinforcement-learning Reinforcement Learning (RL)

VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning

no code implementations26 Oct 2020 Thomas Carta, Subhajit Chaudhury, Kartik Talamadupula, Michiaki Tatsubori

The goal is to force an RL agent to use both text and visual features to predict natural language action commands for solving the final task of cooking a meal.

Atari Games reinforcement-learning +2

Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games

1 code implementation EMNLP 2020 Subhajit Chaudhury, Daiki Kimura, Kartik Talamadupula, Michiaki Tatsubori, Asim Munawar, Ryuki Tachibana

Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art methods despite requiring less number of training episodes.

Q-Learning Reinforcement Learning (RL) +1

Design and Implementation of Linked Planning Domain Definition Language

no code implementations17 Dec 2019 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 autonomous robots.

Common Sense Reasoning

Reinforcement Learning Testbed for Power-Consumption Optimization

1 code implementation21 Aug 2018 Takao Moriyama, Giovanni De Magistris, Michiaki Tatsubori, Tu-Hoa Pham, Asim Munawar, Ryuki Tachibana

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 thermal management.

Systems and Control

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