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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 8 of 8  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
HWS, VLD [detail] 2020-03-05
09:30
Okinawa Okinawa Ken Seinen Kaikan
(Cancelled but technical report was issued)
Monte Carlo Tree Search for routing of delivery drone
Kota Iwasaki, Yasuhiro Takashima (Univ. of Kitakyushu) VLD2019-109 HWS2019-82
In recent years, delivery drone becomes realistic. Thus, it is important to solve the routing problem of delivery drone,... [more] VLD2019-109 HWS2019-82
pp.89-94
CAS, CS 2019-03-08
11:05
Kanagawa Shonan Institute of Technology A Study on the Problem which Monte-Carlo-Tree-Search can Search Efficiently
Tetsuhiro Kitade, Kunihiro Fujiyoshi (TUAT) CAS2018-140 CS2018-108
Monte-Carlo-Tree-Search is expected to be effective for the solution search of a combinatorial optimization problem and ... [more] CAS2018-140 CS2018-108
pp.9-14
VLD, HWS
(Joint)
2018-03-01
10:30
Okinawa Okinawa Seinen Kaikan A Motif Extraction Method Using Monte-Carlo Tree Search and its Experimental Evaluation
Yusuke Yuasa, Shinobu Nagayama, Masato Inagi, Shin'ichi Wakabayashi (Hiroshima City Univ.) VLD2017-108
Discovering a similar substring from multiple strings is called motif extraction. It is used in various fields including... [more] VLD2017-108
pp.115-120
IBISML 2016-03-17
16:10
Tokyo Institute of Statistical Mathematics Bayesian Monte-Carlo tree search method and its application to linear constrained nonlinear control problems
Ryo Otsuki, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) IBISML2015-98
For the nonlinear dynamics where the state transition is nonlinear with respect to the input, in general, we cannot obta... [more] IBISML2015-98
pp.31-38
RECONF 2015-09-18
09:25
Ehime Ehime University Trax solver based on machine-learned evaluation function
Takuya Nakamichi, Yusuke Sonoda, Takayuki Matsuzaki, Motoki Amagasaki, Masahiro Iida, Morihiro Kuga, Toshinori Sueyoshi (Kumamoto Univ.) RECONF2015-33
We develop a solver of board game Trax. Our basic strategy is a common game tree search algorithm. We explore the best m... [more] RECONF2015-33
pp.7-12
CAS, MSS, IPSJ-AL [detail] 2014-11-21
14:10
Okinawa Nobumoto Ohama Memorial Hall (Ishigaki island) A Survey on Generation of Language-Family Tree by Applying Molecular Phylogenetic Approach
Ren Wu (Yamaguchi JC.), Yuya Matsuura, Hiroshi Matsuno (Yamaguchi Univ.) CAS2014-104 MSS2014-68
In recent years, it has become popular to generate language-family trees of linguistics by applying the methods used in ... [more] CAS2014-104 MSS2014-68
pp.147-152
NC, MBE
(Joint)
2014-03-18
13:00
Tokyo Tamagawa University Layered Monte-Carlo planning method for incomplete information games and its application to Puyo-Puyo
Ryo Otsuki, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NC2013-136
Recently, the effectiveness of the game-tree search algorithm based on UCT and its applicability to the game Go have bee... [more] NC2013-136
pp.275-280
CPSY, DC
(Joint)
2009-08-04
- 2009-08-05
Miyagi   Acceleration of Monte-Carlo Go by FPGA-based Hardware
Kenichi Koizumi (Univ. of Tokyo), Yasuo Ishii (NEC Corp.), Kazuki Yoshizoe (JST), Takefumi Miyoshi (Tokyo Inst. of Tech.), Yutaka Sugawara (IBM), Mary Inaba, Kei Hiraki (Univ. of Tokyo) CPSY2009-19
In the monte-carlo simulation of Go, it takes time to run playouts. There were attempts of accelerating
by implementing... [more]
CPSY2009-19
pp.55-60
 Results 1 - 8 of 8  /   
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