Proposal and Evaluation of System of Dynamic Adapting Method to Player s Skill
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1 1,a) , AI AI AI AI 0 AI 3 AI AI AI AI AI AI AI AI AI 5% AI AI Proposal and Evaluation of System of Dynamic Adapting Method to Player s Skill Takafumi Nakamichi 1,a) Takeshi Ito 1 Received: February 19, 2016, Accepted: September 6, 2016 Abstract: Game playing AI which can adjust its skill level dynamically for each opponent player without artificiality has important role in implementing computer-assisted instruction system for game playing. However, the existing methods cannot adjust the skill level of a Shogi program dynamically. In addition to this problem, artificiality with Shogi program made for weak players has not been measured quantitatively. Therefore, the present paper reports on a novel method for dynamic adjustment of the skill level of a Shogi program for each opponent player without artificiality. This method calculates and selects a move which approximates evaluation value of position to zero by game tree search and evaluation value function. Furthermore, this proposed method was evaluated by three experiments. First, ability for winning rate adjustment of the proposed method was evaluated by matches with weak game playing AI. Second, think aloud data and the subjective evaluations of all moves were gathered by matches with novice and person with experience. We compared detection rates of the proposed and existing AI. Finally, the range of players whom strength was matched with proposed AI was confirmed by matches on internet Shogi server. From results of these experiments, proposed AI was shown as proper strength for novice to kyu-players by winning rate and subjective evaluation. In addition, proposed AI makes bad moves more than existing AI, nevertheless, its detection rate of bad moves were lower than 5% for novice players. Keywords: computer Shogi, computer-assistant system, adaptive opponent AI, believability, tutoring Shogi AI 1 The University of Electro-Communications, Chofu, Tokyo , Japan a) nakamichi@minerva.cs.uec.ac.jp 1. AI 1997 IBM Deep Blue c 2016 Information Processing Society of Japan 2426
2 Garry Kasparov [1], [2] Google AI [3], [4] AI 2009 Mercosur Cup Pocket Fritz [5] PC AI Computer Assisted Instruction CAI [6] Burton [7] [8] AI AI AI AI AI AI AI [8] AI 1 AI 3 1 AI AI AI 2 AI 3 AI 3 AI AI AI AI *1 AI AI Bonanza 1 824(1-dan) [9] AI *1 81Dojo 24 81Dojo Rating System System floodgate gpsfish normal 1c AI 2800 AI u-tokyo.ac.jp/shogi/index.html 81Dojo (1-dan) c 2016 Information Processing Society of Japan 2427
3 2 AI AI 2.2 AI AI 2 [10], [11] AI ON/OFF AI AI 20 AI ON/OFF [12] [8] 2 3 AI AI [13] AI 2.3 AI 50% 0 min-max αβ (1) { V (M), V(M) 0 V 0 (M) = (1) V (M), V(M) < AI 2.4 Bonanza [14] Bonanza minmax [12], [15] Bonanza futility pruning null pruning (1) 0 Bonanza [16] Bonanza transposition table hash learn ponder Bonanza transposition table Bonanza AI AI 5 3. AI c 2016 Information Processing Society of Japan 2428
4 1 AI AI 1 4 Bonanza 95% Fig. 1 Winning rate against proposed AI and existing AI. 5 5 AI 5 AI AI AI Bonanza (1-dan) 1159(2-dan) 1442(2-dan) 1740(3-dan) [9] 5 Bonanza 1984(4-dan) 4 AI 5 AI 5 AI AI AI AI 3.2 AI 4 AI 95% 1 AI AI 4 AI AI 4 AI 2 AI 1 AI AI AI AI AI AI 5 4 AI 9 AI AI AI AI AI AI AI AI 2 AI AI AI Cleveland [17] 5 c 2016 Information Processing Society of Japan 2429
5 AI 1 Bonanza 5 Bonanza 5 Bonanza 2 AI AI AI AI AI AI AI 4 AI AI 1 3 AI 10 Table 1 Winning rate and average of the number of moves. 2 3 AI 95% Fig. 2 Subjective degree of strength of 3 opponent players. 3 AI 2 [18], [19] AI Bonanza AI AI 1 1 AI AI 90% AI 1 AI 5 AI AI c 2016 Information Processing Society of Japan 2430
6 2 3 AI AI Table 2 Breakdown of bad moves and detected bad moves AI 3 Fig. 3 Position of undetected bad move. 5 95% 5 AI AI 5 AI AI AI 3 1, AI AI AI AI AI AI 2 AI 1 AI AI 5 AI 1 5% 4.5 AI 2 AI 1 1 AI 5 AI 5 AI AI AI AI AI 2 5% AI 4 3 1, , ,000 c 2016 Information Processing Society of Japan 2431
7 AI AI 5. AI 4 Fig. 4 Figure of undetected move and search space. 1, , ,000 AI 1, AI 2 1, [8] AI 2 1 AI AI AI AI Dojo AI AI AI ID AI AI AI , AI 50 c 2016 Information Processing Society of Japan 2432
8 3 Table 3 Breakdown of respondent. Fig. 6 6 AI Breakdown of subjective strength of proposed AI. 5 AI AI Fig. 5 Expected and actual winning rates of players. 50 1,009 6, SD: Dojo AI AI 5 5 AI 1984(4-dan) AI AI 1984(4-dan) 81Dojo 2000 AI 81Dojo 1984 ( R R) ± 400 R = R + 25 Orange Red AI Grey Purple AI SD: Grey AI (2) 5.4 AI 1 AI AI 0% c 2016 Information Processing Society of Japan 2433
9 AI AI 2 AI Blue Purple 5 AI AI AI AI AI AI AI AI 2 Grey 7 2 AI Blue Purple Grey AI AI 4 AI AI [8] AI 4 AI AI 6. AI 0 AI AI AI AI AI AI AI 3 81dojo Bot JSPS B [1] NC Vol.111, No.419, pp (2012). [2] Vol.54, No.9, pp (2013). [3] Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. and Hassabis, D.: Mastering the Game of Go with Deep Neural Networks and Tree Search, Nature, 529, pp (2016). [4] Alpha Go Vol.57, No.4, pp (2016). [5] Mercosur Cup 2009, available from com/games/mercosur2009/mercosur09.htm. [6] Vol.10, No.6, pp (1995). [7] Burton, R.R. and Brown, J.S.: An investigation of computer coaching for informal learning activities, Internac 2016 Information Processing Society of Japan 2434
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![49, pp.527 568 (2014). [13] 2012 pp.68 75 (2012). [14] Hoki, K.: Bonanza The Computer Shogi Program, available from http://www.geocities.co.jp/ bonanza shogi/ (accessed 2016-05-12). [15] 2006 pp.](/docs-images/92/107755447/images/10-1.jpg "78 83 (2006). [16] Laird, J.E. and Duchi, J.C.: Creating Human-like Synthetic Characters with Multiple Skill Level: A Case Study using the Soar Quakebot, AAAI, pp.54 58 (2000). [17] Cleveland, A.")
10 tional Journal of Man-Machine Studies, Vol.11, No.1, pp.5 24 (1979). [8] Vol.58, No.3, pp (2013). [9] Vol GI-30, No.7, pp.1 7 (2013). [10] Grimbergen, R. AI 2012 pp (2012). [11] AI Vol.2012-GI-27, No.5, pp.1 8 (2012). [12] Hoki, K. and Kaneko, T.: Large-Scale Optimization for Evaluation Functions with Minimax Search, Journal of Artificial Intelligence Research, Vol.49, pp (2014). [13] 2012 pp (2012). [14] Hoki, K.: Bonanza The Computer Shogi Program, available from bonanza shogi/ (accessed ). [15] 2006 pp (2006). [16] Laird, J.E. and Duchi, J.C.: Creating Human-like Synthetic Characters with Multiple Skill Level: A Case Study using the Soar Quakebot, AAAI, pp (2000). [17] Cleveland, A.: The Psychology of Chess and of Learning to Play It, The American Journal of Psychology, Vol.18, No.3, pp (1907). [18] 2014 pp.9 16 (2014). [19] Floodgate Vol.2015-GI-33, No.14, pp.1 4 (2015) c 2016 Information Processing Society of Japan 2435
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