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- おきみち いちぞの
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1 AI
2
3 AI AI AI AI AI AI COM Computer Player NPC Non-Player Character AI AI AI AI AI AI AI AI TCG AI i
4 Infinite Mario Bros. AI AI ii
5 AI AI AI AI AI AI AI AI AI TCG TCG TCG TCG Rule-based iii
6 AI Q A* Infinite Mario Bros Infinite Mario Bros AI AI iv
7 v
8
9 RL-agent RL-agent ( ) RL-agent ( ) Infinite Mario Bros vii
10
11 Q R ix
12
13 [1] 1950 [2] CPU PC IBM Deep Blue [3] AI AI AI AI AI COM Computer Player NPC Non-Player Character 1
14 % [4] AI AI AI AI AI [5, 6, 7] AI [8, 9, 10] AI AI AI AI AI AI AI AI AI AI 1 AI AI 2
15 1.1 AI 1.1 AI AI AI Bonanza [11] Bonanza 6 Bonanza [12, 13] [14, 15] AI 3
16 1 Baumgarten 2009 Mario AI Competition A* AI [16] Mario AI Competition Infinite Mario Bros. AI [17] Baumgarten AI A* Tsay 2009 Mario AI Competition Q AI [18]. Tsay AI 4 AI 1 Fujita Hearts Q AI [19, 20, 21, 22] Hearts 3 2,000 4,000 Hearts 1.2 AI Hearts AI 4
17 1.2 AI CPU PC AI 4 1. AI 2. AI 3. AI 4. AI 1 AI [23, 24, 25] [26, 27] [28] RTS [29, 30] AI AlphaGo [31] RTS AI RTS AI RTS 2 AI 5
18 1 AI AI 3 AI [32, 33, 34] 4 AI AI AI AI 4 AI AI FPS First Person Shooter AI AI [10] [35] [7, 9] 1.3 AI 1.4 AI 6
19 1.3 AI 1.3 AI AI AI AI 3 Polceanu Schrum 2012 The 2K BotPrize 2012 AI [36, 5] The 2K BotPrize FPS Unreal Tournament 2004 AI Polceanu AI Schrum AI 52.2% 51.9% AI 41.4% Polceanu Schrum AI Ortega Infinite Mario Bros. AI [6] 7
20 1 AI [7] Bonanza[11] AI AI AI AI AI 3 AI AI [8] 4 AI Bonanza[11] AI [9] AI [23] AI 8
21 1.4 AI [10] AI AI 1.4 AI 1.2 AI AI AI 9
22 1 AI 1.5 HCI [37, 38] EC [39, 40, 41, 42] [43, 44] EC 10
23 AI AI 1950 [2] 5 30% 1966 Weizenbaum ELIZA[45] 1972 Colby PARRY[46] % Veselov 13 Eugene Goostman 13 1 Goostman 1.3 AI Polceanu Schrum FPS AI[36, 5] AI The 2K BotPrize 2012 The 2K BotPrize 2012 AI 2 3 AI 25 11
24 1 AI The 2K BotPrize 2014 AI The 2K BotPrize 2014 Polceanu AI 2 Goostman 40 Goostman 10 The 2K BotPrize FPS AI[7] 5 AI AI AI 20 5 AI 3 [41, 42] 12
25 1.7 AI EC : AI AI AI AI AI 1 2 AI TCG 13
26 1 AI 1 3 AI AI [47, 48] [49] AI Infinite Mario Bros. 4 AI AI AI 6 14
27 2 AI AI AI 1.4 TCG TCG AI 2.1 TCG [19, 20, 21, 22] TCG Hearts TCG TCG TCG 15
28 2 AI AI TCG AI 4 1. TCG AI TCG TCG TCG TCG TCG TCG TCG 1999 TCG TCG ( ) TCG TCG TCG TCG 16
29 (RPG) TCG TCG ( ) (HP) 0 TCG AI AI AI AI AI AI AI AI TCG TCG TCG 17
30 2 AI TCG TCG 1. AI ( ) ( ) 1. AI ( ) AI
31 TCG TCG AI ( 1 ) 1. AI ( ) AI 1 19
32 2 AI ( ) ( ) 20
33 : 2.2: / TCG 21
34 2 AI TCG TCG AI 15 3 ( ) (HMM) [50] (RNN) [51] [52] TCG [53] [19, 20, 21, 22] 22
35 ( 2.4.1) 2. 行動予測器 相手の次の行動 5. ランダムサンプリング 1. 効用関数現状態である確率 次状態である確率 ( 即時報酬 + 次状態の状態価値 ) 3. 属性相性学習器 4. 状態価値関数 6. 状態圧縮 状態 (32 次元 ) 観測 (25 次元 ) 行動 (6 次元 ) 最大化 2.1: 1. ) AI AI ( ) AI 23
36 2 AI AI AI ( ) AI AI (MLP) MLP (9 1 0) MLP
37 TCG MLP AI ( ) 4. MLP 1 5. TCG 6. TCG 15 25
38 2 AI 1 5 (32 ) 0 17 AI 0-4 AI AI AI 14 AI 15 AI 16 AI 17 AI AI AI AI (25 ) 0 17 AI 0-4 AI AI AI 14 AI 15 AI 16 AI 17 AI 26
39 AI (9 ) TCG AI MLP
40 2 AI MLP 6 ( ) 2. ( ) 10 (10 )
41 MLP AI 3 AI 3 80% 20% TCG MLP 6 29
42 2 AI TCG MLP (RL-agent) (Rule-based) RL-agent Rule-based 100 Rule-based 200 RL-agent TCG 3 Rule-based 200 RL-agent RL-agent Rule-based Rulebased ( Rule-based Rule-based Rule-based) 30
43 Rule-based Rule-based 11 (1) ( ) ( ) Rule-based Rule-based
44 2 AI (RL-agent) ( Rule-based) 5,200 RL-agent 2.2 RL-agent 3 RL-agent 500 RL-agent 2.2: RL-agent RL-agent 25 2,200 80% ( 2.2) RL-agent 50% 500 Rule-based 2.3 RL-agent Rule-based 2.4 RL-agent Rulebased 5,200 RL-agent RL-agent 25 2,200 80% Rule-based 2.2 Rule-based RL-agent 80% Rule-based 32
45 : RL-agent ( ) 2.4: RL-agent ( ) 33
46 2 AI RL-agent Rule-based 2, : ( 2.5) RL-agent 2, % ( 2.6) RL-agent 2.5 RL-agent 70% 80% RL-agent Rule-based 5,200 34
47 : 2.7: 35
48 2 AI 2.8: 40% % 2,500 RL-agent 1, TCG 80% AI TCG ( ) 36
49 2.7 ( ) TCG AI 2.7 TCG TCG TCG 37
50 2 AI AI AI 38
51 3 AI AI AI 1.4 AI AI 3.1 Cabrera [47] [48] Maslow 5 [49] 1) 2) 39
52 3 AI 3) 4) 5) 5) Cabrera [47] [48] Maslow [49] 1. AI 2. AI 3. AI 4. 40
53 Q Q [54] Q Q argmax at Q(s t, a t ) (3.1) (3.1) t s t t a t t Q(s t, a t ) s t a t Q Q s t Q Q Q(s t, a t ) = (1 α)q(s t, a t ) + α((r + γmax p Q(s t+1, p)) (3.2) (3.2) α Q r γ 0 1 r s t a t ϵ greedy ϵ greedy 1 ϵ Q ϵ t s t a t r [55, 56] Q (3.1) (3.2) s t n s t n (3.2) Q r 41
54 3 AI r ϵ s t s t A* A* 1.1 A* A* f (n) = g (n) + h (n) (3.3) 3.3, f (n) n f (n) g (n) n h (n) n A* Infinite Mario Bros. Infinite Mario Bros. Infinite Mario Bros. 2D 3.1 Infinite Mario Bros. 4 1) 2) 3) 42
55 : Infinite Mario Bros. 4) 2D 2D Infinite Mario Bros. 3.1 LEFT, RIGHT, DOWN, SPEED, JUMP 24 43
56 3 AI Mario AI Competition [17] Infinite Mario Bros. Infinite Mario Bros s [16] 2 2 s 44
57 : 8 9 Q a 11 a 3.1 Q r 45
58 3 AI 3.1: (LEFT, RIGHT, DOWN, JUMP, SPEED) (OFF,ON,OFF,OFF,OFF) (OFF,ON,OFF,OFF,ON) (OFF,ON,OFF,ON,OFF) (OFF,ON,OFF,ON,ON) (ON,OFF,OFF,OFF,OFF) (ON,OFF,OFF,OFF,ON) (ON,OFF,OFF,ON,OFF) (ON,OFF,OFF,ON,ON) (OFF,OFF,OFF,ON,OFF) (OFF,OFF,ON,OFF,OFF) (OFF,OFF,OFF,OFF,OFF) r r = distance + damaged + death + keyp ress (3.4) (3.4) distance t t + 1 a t pixel damaged death keyp ress distance pixel 2 damaged 50.0 death keyp ress 5.0 A* 1.1 A* [16] g (n) h (n) 46
59 : Q Q Q 1 Q 2 (pixel) 0 4 (frame)( ) 0(0.0) 3(0.125) (keyp ress =) α γ ϵ ( ) 3.3 Q 2 Q 3.2 Q 1 ϵ Q 2 ϵ ϵ
60 3 AI Q Q A* A* 3.4 Q A* µ = 34 σ = 29 5 µ σ
61 : [, ] [, ] [,, ] ( ) [, ] [, ] [ ] ( ) [ ] ( ) [ ] ( ) µ + σ Infinite Mario Bros Q 3 A* 2 3 Q 2 Q ϵ 0 ϵ
62 3 AI [57] Q A* 3.4 Q [, ] 0.66 [, ] = % % 0.37 < 95% 0.48 Q [, ] [ ][ ][ ] [, ] [ ] 1.12 > 99% 0.58 [, ] [ ] 1.33 > 99% 0.58 [, ] [ ] 0.44 < 95% Q A* Q [, ] 0.66 [, ] = % % :0.37 < 95% :0.48 A* [, ] [, ] 1% :1.35 > 99% :0.72 Q [, ] [ ][ ][ ] A* [, ] 50
63 3.5 [ ][ ] [, ] [ ] :1.12 > 99% :0.58 [, ] [ ] :1.33 > 99% :0.58 [, ] [ ] :0.71 > 95% : [, ] [, ] [, ] [ ] r r < r 5 46 r < r [, ] [, ] 0.62 > 95% 0.57 [ ] 0.74 > 99% [, ] [, ] [, ] 1% 51
64 3 AI [ ] [, ] [ ] 5% [, ] [, ] [, ] [ ] [, ] 3.3 Q s [,, ] [ ]
65 [ ] [58] [59] [60, 61] Minsky 6 [62] 1) 2) 3) 4) 5) 6) 6 2) 3) 53
66 3 AI 1) 1) 1) 2) 3) 4) 5) 6) 3.6 AI 1) 2) 3) 3 54
67 : 3.4: 55
68 3 AI 3.5:
69 4 EC 4.1 [41, 42] Infinite Mario Bros. [63] Super Mario Bros. Q A* 57
70 4 AI AI fnirs( ) 3 (10 10 ) 58
71
72 4 4.1: Infinite Mario Bros. 4.1 Infinite Mario Bros. 60
73 , = RTA RTA 61
74 4 4.1:
75 : RTA , ,
76 4 100 ( % ) 度信確間人 ) / ( 点さ 手上 上手さ 人間確信度 : R R = R R = ,2,7,
77 : :
78 ,4,6, ,3, ,9 4.2 R ,4,6,
79 : : R RTA 67
80
81 5 AI AI AI AI AI AI 2 3 AI AI AI 5.1 AI AI 2 TCG AI AI FPS RTS 3 4 2D AI AI FPS 69
82 5 RTS AI 2 TCG AI 2.6 AI AI 60fps frame per second ms 24fps 41ms 3 2D AI AI 5.2 AI 3 AI [64] 70
83 5.2 AI [65] AI 4 2D AI 2D FPS 71
84
85 6 AI AI AI AI TCG AI Infinite Mario Bros. AI AI 73
86 6 AI AI AI 74
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94 Nobuto Fujii, Mitsuyo Hashida, Haruhiro Katayose, Strategy-acquisition System for Video Trading Card Game, International Conference on Advances in Computer Entertainment Technology 2008 (ACE2008), pp , Keio University Japan, 12/2008 Shingo Hattahara, Nobuto Fujii, Shinpei Nagae, Koji Kazai, Haruhiro Katayose, Brain Activity During Playing Video Game Correlates with Player Level, International Conference on Advances in Computer Entertainment Technology 2008 (ACE 2008), pp , Keio University Japan, 12/ pp /9/26 GA NPC 2015 pp /9/ pp /9/ pp /11/9 Extended abstract Beginner s Glide: 2014 pp /9/13 AI 2014(CEDEC2014) 2014/9/4 AI 82
95 2013 pp /11/9 Extended abstract COM 2013 pp /10/4 18 VRSJ /9/18-20 COM SIG-EC27 Vol EC-27 No /03/ pp /09/ pp / pp /10 SIG-SKL-01 pp /09 Vol.2008 No EC-9 pp. 9-16, 2008/03 83
96 2008 pp / pp /10 Vol.2006 No EC-3 pp / (CEDEC2014) 2014/9/3 2014/3/19 AI 26 JAIST 2014/10/15 84
97 /9/ /08/ /03/14 Best Paper Award Gold International Conference on Advances in Computer Entertainment Technology 2013 (ACE2013)(2013/11/15) /11/ /12/ /09/ / /03 85
98
99 3 8 Unity Technologies Japan / KMD 5 87
100
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![[4], [5] COM [4] COM [5] COM COM [6] Infinite Mario Bros. COM 2 3 4 Infinite Mario Bros. 5 2. 2.1 COM [7] [1], [2] Bonanza [7] Bonanza 6 Bonanza [3],](/thumbs/42/22914485.jpg "[4], [5] COM [4] COM [5] COM COM [6] Infinite Mario Bros. COM 2 3 4 Infinite Mario Bros. 5 2. 2.1 COM [7] [1], [2] Bonanza [7] Bonanza 6 Bonanza [3],")
(EC2013) 2013 10 COM 1,2,a) 1 1 1,b) 1,c) COMCOM COM COM COM COM Evaluating Human-like Video-Game Agents Autonomously Acquired with Biological Constraints Fujii Nobuto 1,2,a) Sato Yuichi 1 Wakama Hironori
2797 4 5 6 7 2. 2.1 COM COM 4) 5) COM COM 3 4) 5) 2 2.2 COM COM 6) 7) 10) COM Bonanza 6) Bonanza 6 10 20 Hearts COM 7) 10) 52 4 3 Hearts 3 2,000 4,000
Vol. 50 No. 12 2796 2806 (Dec. 2009) 1 1, 2 COM TCG COM TCG COM TCG Strategy-acquisition System for Video Trading Card Game Nobuto Fujii 1 and Haruhiro Katayose 1, 2 Behavior and strategy of computers
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Convolutional Neural Network 2014 3 A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolutional Neural Network Fukui Hiroshi 1940 1980 [1] 90 3
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1,a),3,b) Application of Saliency Extraction Methods to Neural Networks in Shogi Taichi Nakayashiki 1,a) Tomoyuki Kaneko,3,b) Abstract: Computer shogi programs defeated human experts and it has been said
Haiku Generation Based on Motif Images Using Deep Learning Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura Scho
Haiku Generation Based on Motif Images Using Deep Learning 1 2 2 2 Koki Yoneda 1 Soichiro Yokoyama 2 Tomohisa Yamashita 2 Hidenori Kawamura 2 1 1 School of Engineering Hokkaido University 2 2 Graduate
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26 Development of Learning Support System for Fixation of Basketball Shoot Form 1175094 ,.,,.,,.,,.,,,.,,,,.,,,.,,,,, Kinect i Abstract Development of Learning Support System for Fixation of Basketball
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ol2013-nl-214 No6 1,a) 2,b) n-gram 1 M [1] (TG: Tree ubstitution Grammar) [2], [3] TG TG 1 2 a) ohno@ilabdoshishaacjp b) khatano@maildoshishaacjp [4], [5] [6] 2 Pitman-Yor 3 Pitman-Yor 1 21 Pitman-Yor
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TV 1,2,a) 1 2 2015 1 26, 2015 5 21 Augmented TV TV AR Augmented Reality 3DCG TV Estimation of TV Screen Position and Rotation Using Mobile Device Hiroyuki Kawakita 1,2,a) Toshio Nakagawa 1 Makoto Sato
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PAC Facework from the Perspectives of Interactions between Members: A Case Study of a Japanese Language Classroom by PAC analysis YOKOMIZO Tamaki The purpose of this study is to investigate how facework
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Vol.55 No.1 2 15 (Jan. 2014) 1,a) 2,3,b) 4,3,c) 3,d) 2013 3 18, 2013 10 9 saccess 1 1 saccess saccess Design and Implementation of an Online Tool for Database Education Hiroyuki Nagataki 1,a) Yoshiaki
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25 Study on Rating System in Multi-player Games with Imperfect Information 1165069 2014 2 28 2 ( ) i ii Abstract Study on Rating System in Multi-player Games with Imperfect Information Shigehiko MORITA
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1 1,2,a) 1 1 2016 2 19, 2016 9 6 1 Convolutional Neural Network; CNN 1 / / 13 1 CNN CNN 2 Convolutional Neural Network Estimating Player s Strength by CNN from One Game Record of Go Nobuo Araki 1,2,a)
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[II] Optimization Computation for 3-D Understanding of Images [II]: Ellipse Fitting 1. (1) 2. (2) (edge detection) (edge) (zero-crossing) Canny (Canny operator) (3) 1(a) [I] [II] [III] [IV ] E-mail sugaya@iim.ics.tut.ac.jp
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CodeDrummer: 1 2 3 1 CodeDrummer: Sonification Methods of Function Calls in Program Execution Kazuya Sato, 1 Shigeyuki Hirai, 2 Kazutaka Maruyama 3 and Minoru Terada 1 We propose a program sonification
Consideration of Cycle in Efficiency of Minority Game T. Harada and T. Murata (Kansai University) Abstract In this study, we observe cycle in efficien
Consideration of Cycle in Efficiency of Minority Game T. Harada and T. Murata (Kansai University) Abstract In this study, we observe cycle in efficiency of Minority Game. The Minority Game is a game when
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4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
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x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
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1 1 1 2 DCRA 1. 1.1 1) 1 Tactile Interface with Air Jets for Floating Images Aya Higuchi, 1 Nomin, 1 Sandor Markon 1 and Satoshi Maekawa 2 The new optical device DCRA can display floating images in free
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a) Change Detection Using Joint Intensity Histogram Yasuyo KITA a) 2 (0 255) (I 1 (x),i 2 (x)) I 2 = CI 1 (C>0) (I 1,I 2 ) (I 1,I 2 ) 2 1. [1] 2 [2] [3] [5] [6] [8] Intelligent Systems Research Institute,
1: A/B/C/D Fig. 1 Modeling Based on Difference in Agitation Method artisoc[7] A D 2017 Information Processing
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1,a) 2,b) 3 Modeling of Agitation Method in Automatic Mahjong Table using Multi-Agent Simulation Hiroyasu Ide 1,a) Takashi Okuda 2,b) Abstract: Automatic mahjong table refers to mahjong table which automatically
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1, Adobe Illustrator Photoshop [1] [2] [3] Initital Values Assignment of Parameters Using Onomatopoieas for Interactive Design Tool Tsuyoshi NAKAMURA, Yuki SAWAMURA, Masayoshi KANOH, and Koji YAMADA Graduate
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JAIST Reposi Title 少数の記録からプレイヤの価値観を機械学習するチー ムプレイ AI の構成 Author(s) 和田, 堯之 ; 佐藤, 直之 ; 池田, 心 Citation 研究報告ゲーム情報学 (GI), 2015-GI-33(5): 1-
JAIST Reposi https://dspace.j Title 少数の記録からプレイヤの価値観を機械学習するチー ムプレイ AI の構成 Author(s) 和田, 堯之 ; 佐藤, 直之 ; 池田, 心 Citation 研究報告ゲーム情報学 (GI), 2015-GI-33(5): 1-8 Issue Date 2015-02-26 Type Journal Article Text version
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1,a) 2 2 1 2,b) 3,c) A choreographic authoring system reflecting a user s preference Ryo Kakitsuka 1,a) Kosetsu Tsukuda 2 Satoru Fukayama 2 Naoya Iwamoto 1 Masataka Goto 2,b) Shigeo Morishima 3,c) Abstract:
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1,a) 2,b) 4,c) 3,d) 4,e) Web A Review Supporting System for Whiteboard Logging Movies Based on Notes Timeline Taniguchi Yoshihide 1,a) Horiguchi Satoshi 2,b) Inoue Akifumi 4,c) Igaki Hiroshi 3,d) Hoshi
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JAIST Reposi https://dspace.j Title ターン制戦略ゲームにおけるベンチマークマップの提 案 Author(s) 木村, 富宏 ; 池田, 心 Citation ゲームプログラミングワークショップ 2016 論文集, 2016: 36-43 Issue Date 2016-10-28 Type Conference Paper Text version author
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Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
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