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| Presentation | 2021-05-21 11:30 Design of a Machine Learner for Adapting Competitive Game Strategies to Players' Proficiency Daisuke Takeuchi, Masami Noro, Atsushi Sawada (Nanzan Univ.)  KBSE2021-2 SWIM2021-2 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent, player modeling has become an important issue in the area of game AI design and many researchers and practitioners are investigating modeling methods which can bring good results. The mechanism for adapting game strategies to the players' proficiency changing over time is one of the keys to attractive game design. This study focuses on the mechanism for estimating players' proficiency which may affect their ways of playing in role-playing games (RPGs). We have defined a set of data for estimating players' proficiency and designed an LSTM based machine learner. Also, we have designed a software architecture for dynamically changing game strategies based on estimated players' proficiency. This architecture can be a common basis for game strategy adaptation. Experimental results using a simple RPG show validity of our proposal. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Game Player Modeling / Player's Proficiency / Game Engine / Machine Learning / LSTM / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 35, KBSE2021-2, pp. 7-12, May 2021. | |
| Paper # | KBSE2021-2 | |
| Date of Issue | 2021-05-14 (KBSE, SWIM) | |
| ISSN | 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) | |
| Download PDF | KBSE2021-2 SWIM2021-2 |
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| Conference Information | |
| Committee | KBSE SWIM |
| Conference Date | 2021-05-21 - 2021-05-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | KBSE |
| Conference Code | 2021-05-KBSE-SWIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Design of a Machine Learner for Adapting Competitive Game Strategies to Players' Proficiency |
| Sub Title (in English) | |
| Keyword(1) | Game Player Modeling |
| Keyword(2) | Player's Proficiency |
| Keyword(3) | Game Engine |
| Keyword(4) | Machine Learning |
| Keyword(5) | LSTM |
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| Keyword(8) | |
| 1st Author's Name | Daisuke Takeuchi |
| 1st Author's Affiliation | Nanzan Univercity (Nanzan Univ.) |
| 2nd Author's Name | Masami Noro |
| 2nd Author's Affiliation | Nanzan Univercity (Nanzan Univ.) |
| 3rd Author's Name | Atsushi Sawada |
| 3rd Author's Affiliation | Nanzan Univercity (Nanzan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-05-21 11:30:00 |
| Presentation Time | 30 minutes |
| Registration for | KBSE |
| Paper # | KBSE2021-2, SWIM2021-2 |
| Volume (vol) | vol.121 |
| Number (no) | no.35(KBSE), no.36(SWIM) |
| Page | pp.7-12 |
| #Pages | 6 |
| Date of Issue | 2021-05-14 (KBSE, SWIM) |
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