Game-theoretic implications for uncovering the effects of virtual tipping in complex user networks

Authors: Shintaro Ueki, Fujio Toriumi, Toshiharu Sugawara

Source: Applied Network Science, Vol 9, Iss 1, Pp 1-27 (2024)

Subject Terms: Monetary reward, Game theory, Social live streaming services, Virtual tipping, Applied mathematics. Quantitative methods, T57-57.97

File Description: electronic resource

Relation: https://doaj.org/toc/2364-8228

Access URL: https://doaj.org/article/715eb6623eaf4b148ccbcb64ebd01f6d

Locally Centralized Execution for Less Redundant Computation in Multi-Agent Cooperation

Authors: Yidong Bai, Toshiharu Sugawara

Source: Information, Vol 15, Iss 5, p 279 (2024)

Subject Terms: cooperation, multi-agent deep reinforcement learning, redundant computation, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://www.mdpi.com/2078-2489/15/5/279; https://doaj.org/toc/2078-2489

Access URL: https://doaj.org/article/4d99cc3690504944808f13cc531c5018

Two-stage reward allocation with decay for multi-agent coordinated behavior for sequential cooperative task by using deep reinforcement learning

Authors: Yuki Miyashita, Toshiharu Sugawara

Source: Autonomous Intelligent Systems, Vol 2, Iss 1, Pp 1-18 (2022)

Subject Terms: Cooperation, Coordination, Divisional cooperation, Multi-agent deep reinforcement learning, Electronic computers. Computer science, QA75.5-76.95, Computer engineering. Computer hardware, TK7885-7895

File Description: electronic resource

Relation: https://doaj.org/toc/2730-616X

Access URL: https://doaj.org/article/6f38562147ca433b9b1f5be0d6eb883e

eDA3-X: Distributed Attentional Actor Architecture for Interpretability of Coordinated Behaviors in Multi-Agent Systems

Authors: Yoshinari Motokawa, Toshiharu Sugawara

Source: Applied Sciences, Vol 13, Iss 14, p 8454 (2023)

Subject Terms: multi-agent deep reinforcement learning, explainable reinforcement learning, distributed system, attentional mechanism, coordination, cooperation, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/13/14/8454; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/8d9efa7a731b4ddfadbd8009cedbaa51

Estimation of sensor-network topology from time-series sensor data using ant colony optimization method

Authors: Kensuke Takahashi, Toshiharu Sugawara

Source: 2008 IEEE Swarm Intelligence Symposium Swarm Intelligence Symposium, 2008. SIS 2008. IEEE. :1-6 Sep, 2008

Relation: 2008 IEEE Swarm Intelligence Symposium (SIS)

Performance variation due to interference among a large number of self-interested agents

Authors: Toshiharu Sugawara, Toshio Hirotsu, Satoshi Kurihara, Kensuke Fukuda

Source: 2007 IEEE Congress on Evolutionary Computation Evolutionary Computation, 2007. CEC 2007. IEEE Congress on. :766-773 Sep, 2007

Relation: 2007 IEEE Congress on Evolutionary Computation

Learning and relearning of target decision strategies in continuous coordinated cleaning tasks with shallow coordination.

Authors: Keisuke Yoneda¹, k.yoneda@isl.cs.waseda.ac.jp, Ayumi Sugiyama¹, a.sugiyama@isl.cs.waseda.ac.jp, Chihiro Kato¹, c.kato@isl.cs.waseda.ac.jp, Toshiharu Sugawara¹, sugawara@waseda.jp

Source: Web Intelligence (2405-6456); 12/01/2015, p279-294, 16p

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