H
H. Matsubara
Researcher at Future University in Egypt
Publications - 7
Citations - 592
H. Matsubara is an academic researcher from Future University in Egypt. The author has contributed to research in topics: Search and rescue & Multi-agent system. The author has an hindex of 5, co-authored 7 publications receiving 576 citations.
Papers
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Proceedings ArticleDOI
RoboCup Rescue: search and rescue in large-scale disasters as a domain for autonomous agents research
Hiroaki Kitano,Satoshi Tadokoro,Itsuki Noda,H. Matsubara,Tomoichi Takahashi,A. Shinjou,Susumu Shimada +6 more
TL;DR: Detailed analysis on the task domain is presented and characteristics necessary for multi-agent systems for this domain are elucidated.
Proceedings ArticleDOI
The RoboCup-Rescue project: a robotic approach to the disaster mitigation problem
Satoshi Tadokoro,Hiroaki Kitano,Tomoichi Takahashi,Itsuki Noda,H. Matsubara,Atsushi Shinjoh,T. Koto,Ikuo Takeuchi,H. Takahashi,Fumitoshi Matsuno,Mitsunori Hatayama,Jun Nobe,Susumu Shimada +12 more
TL;DR: This paper introduces the RoboCup-Rescue Simulation Project, a contribution to the disaster mitigation, search and rescue problem, a comprehensive urban disaster simulator constructed on distributed computers that provides a virtual reality training function for the public.
Proceedings ArticleDOI
MIKE: an automatic commentary system for soccer
TL;DR: This paper describes MIKE, an automatic commentary system for the game of soccer that interprets this domain with six soccer analysis modules that run concurrently within a role-sharing framework and discusses how to control the interaction between them.
Proceedings ArticleDOI
Multi-agent Monte Carlo Go
TL;DR: A Multi-Agent version of UCT Monte Carlo Go is proposed, using the emergent behavior of a great number of simple agents to increase the quality of the Monte Carlo simulations, increasing the strength of the artificial player as a whole.
Proceedings ArticleDOI
RoboCup as a strategic initiative to advance technologies
TL;DR: While the practical issues have been mainly attacked in the real robot leagues, the more strategic issues in multi-agent environments have been focused in the simulation league, such as teamwork among agents, agent modeling, and multi- agent learning which are argued in the rest of the paper.