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https://hdl.handle.net/10119/14266
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| Title: | Detection and Labeling of Bad Moves for Coaching Go |
| Authors: | Ikeda, Kokolo
Viennot, Simon
Sato, Naoyuki |
| Keywords: | Coaching Go
Educational Computer Player
The game of Go
Machine Learning
Bad Mode Detection |
| Issue Date: | 2016-09 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Magazine name: | IEEE Conference on Computational Intelligence and Games (CIG2016) |
| Start page: | 1 |
| End page: | 8 |
| DOI: | 10.1109/CIG.2016.7860441 |
| Abstract: | The level of computer programs has now reached professional strength for many games, even for the game of Go recently. A more difficult task for computer intelligence now is to create a program able to coach human players, so that they can improve their play. In this paper, we propose a method to detect and label the bad moves of human players for the game of Go. This task is challenging because even strong human players only agree at a rate of around 50% about which moves should be considered as bad. We use supervised learning with features largely available in many Go programs, and we obtain an identification level close to the one observed between strong human players. Also, an evaluation by a professional player shows that our method is already useful for intermediate-level players. |
| Rights: | This is the author's version of the work. Copyright (C) 2016 IEEE. IEEE Conference on Computational Intelligence and Games (CIG2016), 2016, 1-8. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | https://hdl.handle.net/10119/14266 |
| Material Type: | author |
| Appears in Collections: | b10-1. 雑誌掲載論文 (Journal Articles)
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