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Yuki Miyashita

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2020 – today

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2025
[c23]
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  - conf/icaart/ShimadaMS25
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Daiki Shimada, Yuki Miyashita, Toshiharu Sugawara:
Multi-Agent Path Finding Using Provisionally Booking Nodes for Pickup and Delivery Problems. ICAART (3) 2025: 528-537
2024
[c22]
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Yuki Miyashita, Toshiharu Sugawara:
Scheduling and Negotiation Method for Double Synchronized Multi-Agent Pickup and Delivery Problem. ICAART (1) 2024: 321-332
[c21]
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  - conf/icmla/NakasoneMMS24
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Gentoku Nakasone, Yoshinari Motokawa, Yuki Miyashita, Toshiharu Sugawara:
Efficient Retraining for Continuous Operability Through Strategic Directives. ICMLA 2024: 1382-1389
[c20]
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Daiki Shimada, Yuki Miyashita, Toshiharu Sugawara:
Path Finding with Flexible Provisional Booking in Multi-agent Pickup and Delivery Problems. PRIMA 2024: 74-80
[c19]
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  - conf/webi/FujisawaSMS24
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Yosuke Fujisawa, Daiki Shimada, Yuki Miyashita, Toshiharu Sugawara:
Deep Reinforcement Learning with Local Communications for Continuous Pathfinding Problems in Multi-Agent Systems. WI/IAT 2024: 265-272
2023
[j3]
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  - journals/sncs/YamauchiMS23
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Tomoki Yamauchi, Yuki Miyashita, Toshiharu Sugawara:
Efficient Path and Action Planning Method for Multi-Agent Pickup and Delivery Tasks under Environmental Constraints. SN Comput. Sci. 4(1): 83 (2023)
[c18]
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Yuki Miyashita, Tomoki Yamauchi, Toshiharu Sugawara:
Distributed Planning with Asynchronous Execution with Local Navigation for Multi-agent Pickup and Delivery Problem. AAMAS 2023: 914-922
[i3]
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  - journals/corr/abs-2302-09250
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Yuki Miyashita, Tomoki Yamauchi, Toshiharu Sugawara:
Distributed Planning with Asynchronous Execution with Local Navigation for Multi-agent Pickup and Delivery Problem. CoRR abs/2302.09250 (2023)
2022
[j2]
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  - journals/auinsy/MiyashitaS22
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Yuki Miyashita, Toshiharu Sugawara:
Two-stage reward allocation with decay for multi-agent coordinated behavior for sequential cooperative task by using deep reinforcement learning. Auton. Intell. Syst. 2(1): 1-18 (2022)
[c17]
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Tomoki Yamauchi, Yuki Miyashita, Toshiharu Sugawara:
Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks. AAMAS 2022: 1427-1435
[c16]
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  - conf/compsac/MiyashitaYS22
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Yuki Miyashita, Tomoki Yamauchi, Toshiharu Sugawara:
Distributed and Asynchronous Planning and Execution for Multi-agent Systems through Short-Sighted Conflict Resolution. COMPSAC 2022: 14-23
[c15]
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Yuki Miyashita, Toshiharu Sugawara:
Flexible Exploration Strategies in Multi-Agent Reinforcement Learning for Instability by Mutual Learning. ICMLA 2022: 579-584
[c14]
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  - conf/kes/FujitaniYMS22
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Yukita Fujitani, Tomoki Yamauchi, Yuki Miyashita, Toshiharu Sugawara:
Deadlock-Free Method for Multi-Agent Pickup and Delivery Problem Using Priority Inheritance with Temporary Priority. KES 2022: 1552-1561
[c13]
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Yoshihiro Oguni, Yuki Miyashita, Toshiharu Sugawara:
Shifting Reward Assignment for Learning Coordinated Behavior in Time-Limited Ordered Tasks. PAAMS 2022: 294-306
[c12]
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  - conf/prima/YamauchiMS22
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Tomoki Yamauchi, Yuki Miyashita, Toshiharu Sugawara:
Task Selection Algorithm for Multi-Agent Pickup and Delivery with Time Synchronization. PRIMA 2022: 458-474
[i2]
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  - journals/corr/abs-2201-06014
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Tomoki Yamauchi, Yuki Miyashita, Toshiharu Sugawara:
Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks. CoRR abs/2201.06014 (2022)
[i1]
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  - journals/corr/abs-2205-12504
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Yukita Fujitani, Tomoki Yamauchi, Yuki Miyashita, Toshiharu Sugawara:
Deadlock-Free Method for Multi-Agent Pickup and Delivery Problem Using Priority Inheritance with Temporary Priority. CoRR abs/2205.12504 (2022)
2021
[j1]
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  - journals/apin/MiyashitaS21
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Yuki Miyashita, Toshiharu Sugawara:
Analysis of coordinated behavior structures with multi-agent deep reinforcement learning. Appl. Intell. 51(2): 1069-1085 (2021)
[c11]
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  - conf/eumas/YamauchiMS21
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Tomoki Yamauchi, Yuki Miyashita, Toshiharu Sugawara:
Path and Action Planning in Non-uniform Environments for Multi-agent Pickup and Delivery Tasks. EUMAS 2021: 37-54
2020
[c10]
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  - conf/iconip/MiyashitaS20
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Yuki Miyashita, Toshiharu Sugawara:
Coordinated Behavior for Sequential Cooperative Task Using Two-Stage Reward Assignment with Decay. ICONIP (2) 2020: 257-269
[c9]
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  - conf/prima/SmithMS20
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Ken Smith, Yuki Miyashita, Toshiharu Sugawara:
Analysis of Coordination Structures of Partially Observing Cooperative Agents by Multi-agent Deep Q-Learning. PRIMA 2020: 150-164

2010 – 2019

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2019
[c8]
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  - conf/atal/MiyashitaS19
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Yuki Miyashita, Toshiharu Sugawara:
Coordination Structures Generated by Deep Reinforcement Learning in Distributed Task Executions. AAMAS 2019: 2129-2131
[c7]
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  - conf/icann/MiyashitaS19
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Yuki Miyashita, Toshiharu Sugawara:
Cooperation and Coordination Regimes by Deep Q-Learning in Multi-agent Task Executions. ICANN (1) 2019: 541-554
[c6]
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Yuki Miyashita, Toshiharu Sugawara:
Coordination in Collaborative Work by Deep Reinforcement Learning with Various State Descriptions. PRIMA 2019: 550-558
2016
[c5]
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  - conf/icaart/HayanoMS16a
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Masashi Hayano, Yuki Miyashita, Toshiharu Sugawara:
Adaptive Switching Behavioral Strategies for Effective Team Formation in Changing Environments. ICAART (Revised Selected Papers) 2016: 37-55
[c4]
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Masashi Hayano, Yuki Miyashita, Toshiharu Sugawara:
Switching Behavioral Strategies for Effective Team Formation by Autonomous Agent Organization. ICAART (1) 2016: 56-65
[c3]
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  - conf/isami/MiyashitaOPMVC16
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Yuki Miyashita, Mahiro Oura, Juan Francisco de Paz, Kenji Matsui, Gabriel Villarrubia, Juan M. Corchado:
Preliminary Study of Classifier Fusion Based Indoor Positioning Method. ISAmI 2016: 161-166
2015
[c2]
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  - conf/atal/MiyashitaHS15
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Yuki Miyashita, Masashi Hayano, Toshiharu Sugawara:
Formation of Association Structures Based on Reciprocity and Their Performance in Allocation Problems. COIN@AAMAS/IJCAI 2015: 262-281
[c1]
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Yuki Miyashita, Masashi Hayano, Toshiharu Sugawara:
Self-Organizational Reciprocal Agents for Conflict Avoidance in Allocation Problems. SASO 2015: 150-155

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