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ORCIDFumito Uwano
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2020 – today
- 2024
[j6]Fumito Uwano
, Satoshi Hasegawa, Keiki Takadama:
Inverse Reinforcement Learning with Agents' Biased Exploration Based on Sub-Optimal Sequential Action Data. J. Adv. Comput. Intell. Intell. Informatics 28(2): 380-392 (2024)- [c25]Fumito Uwano, Will Neil Browne:
Cognitive Learning System for Sequential Aliasing Patterns of States in Multistep Decision-Making. GECCO Companion 2024: 315-318 - [c24]Fumito Uwano, Ran'u Kobayashi, Manabu Ohta:
Automatic Extraction of User-Centric Aspects for Tourist Spot Recommender Systems Using Reviews in Japanese. HCI (8) 2024: 245-260 - [c23]Ryota Kubo, Fumito Uwano, Manabu Ohta:
Reward Design for Deep Reinforcement Learning Towards Imparting Commonsense Knowledge in Text-Based Scenario. ICAART (3) 2024: 1213-1220 - 2023
- [c22]Fumito Uwano, Keiki Takadama:
Reinforcement Learning in Cyclic Environmental Changes for Agents in Non-Communicative Environments: A Theoretical Approach. EXTRAAMAS 2023: 143-159 - [c21]Fumito Uwano, Will N. Browne:
Hierarchical Frames-of-References in Learning Classifier Systems. GECCO Companion 2023: 335-338 - [c20]Fumito Uwano:
Implicit Cooperative Learning on Distribution of Received Reward in Multi-Agent System. ICAART (1) 2023: 147-153 - [c19]Manabu Ohta, Hiroyuki Aoyagi, Fumito Uwano, Teruhito Kanazawa, Atsuhiro Takasu:
An End-to-End Table Structure Analysis Method Using Graph Attention Networks. ICADL (2) 2023: 230-239 - 2022
- [c18]Fumito Uwano, Daiki Yamane, Keiki Takadama:
Design of Human-Agent-Group Interaction for Correct Opinion Sharing on Social Media. HCI (4) 2022: 146-165 - [c17]Fumito Uwano:
LSTM-based Abstraction of Hetero Observation and Transition in Non-Communicative Multi-Agent Reinforcement Learning. ICAART (1) 2022: 172-179 - [c16]Hiroyuki Aoyagi, Teruhito Kanazawa, Atsuhiro Takasu, Fumito Uwano, Manabu Ohta:
Table-structure Recognition Method Consisting of Plural Neural Network Modules. ICPRAM 2022: 542-549 - 2021
- [c15]Fumito Uwano:
A Cooperative Learning Method for Multi-Agent System with Different Input Resolutions. ISAMSR 2021: 84-90 - [i1]Fumito Uwano, Keiki Takadama:
Directionality Reinforcement Learning to Operate Multi-Agent System without Communication. CoRR abs/2110.05773 (2021) - 2020
- [j5]Fumito Uwano, Keiki Takadama:
Reward Value-Based Goal Selection for Agents' Cooperative Route Learning Without Communication in Reward and Goal Dynamism. SN Comput. Sci. 1(3): 182 (2020) - [c14]Yoshimiki Maekawa, Fumito Uwano, Eiki Kitajima, Keiki Takadama:
How to Emote for Consensus Building in Virtual Communication. HCI (5) 2020: 194-205
2010 – 2019
- 2019
- [c13]Takuya Iwase, Ryo Takano, Fumito Uwano, Hiroyuki Sato, Keiki Takadama:
Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions. CEC 2019: 800-807 - [c12]Yoshimiki Maekawa, Fumito Uwano, Eiki Kitajima, Keiki Takadama:
How to Design Adaptable Agents to Obtain a Consensus with Omoiyari. HCI (4) 2019: 462-475 - 2018
- [c11]Akari Tobaru, Fumito Uwano, Takuya Iwase, Kazuma Matsumoto, Ryo Takano, Yusuke Tajima, Yuta Umenai, Keiki Takadama:
Improving Sleep Stage Estimation Accuracy by Circadian Rhythm Extracted from a Low Frequency Component of Heart Rate. AAAI Spring Symposia 2018 - [c10]Fumito Uwano, Keiki Takadama:
Ensemble Heart Rate Extraction Method for Biological Data from Water Pressure Sensor. AAAI Spring Symposia 2018 - [c9]Yuta Umenai, Fumito Uwano, Hiroyuki Sato, Keiki Takadama:
Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change. GECCO (Companion) 2018: 97-98 - [c8]Fumito Uwano, Koji Dobashi, Keiki Takadama, Tim Kovacs:
Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining. GECCO (Companion) 2018: 1465-1472 - [c7]Eiki Kitajima, Caili Zhang, Haruyuki Ishii, Fumito Uwano, Keiki Takadama:
Correcting Wrongly Determined Opinions of Agents in Opinion Sharing Model. HCI (4) 2018: 658-676 - [c6]Fumito Uwano, Keiki Takadama:
Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems. PRIMA 2018: 663-670 - 2017
- [j4]Takato Okudo, Tomohiro Yamaguchi, Akinori Murata, Takato Tatsumi, Fumito Uwano, Keiki Takadama:
Supporting the Exploration of the Learning Goals for a Continuous Learner Toward Creative Learning. J. Adv. Comput. Intell. Intell. Informatics 21(5): 907-916 (2017) - [j3]Fumito Uwano, Keiki Takadama:
Comparison Between Reinforcement Learning Methods with Different Goal Selections in Multi-Agent Cooperation. J. Adv. Comput. Intell. Intell. Informatics 21(5): 917-929 (2017) - [j2]Fumito Uwano, Yusuke Tajima, Akinori Murata, Keiki Takadama:
Recovery System Based on Exploration-Biased Genetic Algorithm for Stuck Rover in Planetary Exploration. J. Robotics Mechatronics 29(5): 877-886 (2017) - [c5]Yuta Umenai, Fumito Uwano, Hiroyuki Sato, Keiki Takadama:
Strategies to Improve Cuckoo Search Toward Adapting Randomly Changing Environment. ICSI (1) 2017: 573-582 - 2016
- [j1]Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs:
Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach. EAI Endorsed Trans. Collab. Comput. 2(8): e2 (2016) - [c4]Tomohiro Harada, Fumito Uwano, Takahiro Komine, Yusuke Tajima, Takahiro Kawashima, Morito Morishima, Keiki Takadama:
Real-Time Sleep Stage Estimation from Biological Data with Trigonometric Function Regression Model. AAAI Spring Symposia 2016 - [c3]Yuta Umenai, Fumito Uwano, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, Keiki Takadama:
A modified cuckoo search algorithm for dynamic optimization problems. CEC 2016: 1757-1764 - [c2]Fumito Uwano, Keiki Takadama:
Proceedings in Adaptation, Learning and Optimization. IES 2016: 453-467 - 2015
- [c1]Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs:
Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach. BICT 2015: 332-339
Coauthor Index
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