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Paper Abstract and Keywords
Presentation 2015-09-18 09:25
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
Abstract (in Japanese) (See Japanese page) 
(in English) We develop a solver of board game Trax. Our basic strategy is a common game tree search algorithm. We explore the best move by the alpha-beta pruning. The game tree search is required evaluation function of some sort. we create it by machine learning. Usually this function is made by machine learning based on game record data of expert, but in this game, there is not enough game record data. Therefore, we make the evaluation function by machine learning based on the winning percentage by the Monte Carlo tree search. The search possible number of nodes is small in software-only solver. So we implement a partial of it on FPGA(Field Programmable Gate Array) for acceleration. The solver was about 10 times faster than a software-only solver.
Keyword (in Japanese) (See Japanese page) 
(in English) Accelerator / Machine learning / Monte Carlo tree search / Trax / FPGA / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 228, RECONF2015-33, pp. 7-12, Sept. 2015.
Paper # RECONF2015-33 
Date of Issue 2015-09-11 (RECONF) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Notes on Review This article is a technical report without peer review, and its polished version will be published elsewhere.
Download PDF RECONF2015-33

Conference Information
Committee RECONF  
Conference Date 2015-09-18 - 2015-09-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Ehime University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Reconfigurable Systems, etc. 
Paper Information
Registration To RECONF 
Conference Code 2015-09-RECONF 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Trax solver based on machine-learned evaluation function 
Sub Title (in English)  
Keyword(1) Accelerator  
Keyword(2) Machine learning  
Keyword(3) Monte Carlo tree search  
Keyword(4) Trax  
Keyword(5) FPGA  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Takuya Nakamichi  
1st Author's Affiliation Kumamoto University (Kumamoto Univ.)
2nd Author's Name Yusuke Sonoda  
2nd Author's Affiliation Kumamoto University (Kumamoto Univ.)
3rd Author's Name Takayuki Matsuzaki  
3rd Author's Affiliation Kumamoto University (Kumamoto Univ.)
4th Author's Name Motoki Amagasaki  
4th Author's Affiliation Kumamoto University (Kumamoto Univ.)
5th Author's Name Masahiro Iida  
5th Author's Affiliation Kumamoto University (Kumamoto Univ.)
6th Author's Name Morihiro Kuga  
6th Author's Affiliation Kumamoto University (Kumamoto Univ.)
7th Author's Name Toshinori Sueyoshi  
7th Author's Affiliation Kumamoto University (Kumamoto Univ.)
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Speaker Author-1 
Date Time 2015-09-18 09:25:00 
Presentation Time 25 minutes 
Registration for RECONF 
Paper # RECONF2015-33 
Volume (vol) vol.115 
Number (no) no.228 
Page pp.7-12 
#Pages 6 
Date of Issue 2015-09-11 (RECONF) 

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