Vol. 52 No (Dec. 2011) Ms. Pac-Man IEEE CIG Ms. Pac-Man Ms. Pac-Man AI AI Ms. Pac-Man Ms. Pac-Man Competition Ms. Pac-Man Monte
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1 Vol. 52 No (Dec. 2011) Ms. Pac-Man IEEE CIG Ms. Pac-Man Ms. Pac-Man AI AI Ms. Pac-Man Ms. Pac-Man Competition Ms. Pac-Man Monte-Carlo Tree Search in Ms. Pac-Man Nozomu Ikehata 1 and Takeshi Ito 2 The competition of controller program for Ms. Pac-Man has been held in the CIG symposium of IEEE every year from Since this competition was held, Ms. Pac-Man has become to an attractive subject of research on digital game AI. In the competition by this year, the classical AI method by using the knowledge base has gotten the best result. But, since the improvement by this method is becoming a limit, a new approach is required. In this paper, we realized the Ms. Pac-Man controller by the using Monte-Carlo tree search which is effective on Go, and examined the effectiveness. By repeating the simulation about the future phase generated with the random number, the Monte Carlo tree search can select the next move with a high expected value, without depending on professional expertise. In an evaluation experiment, the controller by the Monte Carlo tree search showed results more excellent than all the programs which participated in Ms. Pac-Man Competition in the past. 1. Ms. Pac-Man 1 AI Ms. Pac-Man AI Competition Ms. Pac-Man 1) Ms. Pac-Man AI Ms. Pac-Man AI Competition AI Ms. Pac-Man Ms. Pac-Man Ms. Pac-Man 1 Ms. Pac-Man 1 1 Konami Digital Entertainment Co., Ltd. 2 The University of Electro-Communications 1 Ms. Pac-Man 3817 c 2011 Information Processing Society of Japan
2 3818 Ms. Pac-Man Ms. Pac-Man Ms. Pac-Man Ms. Pac-Man UCT UCB applied to Trees UCT 1 Ms. Pac-Man 2. Ms. Pac-Man Competition Ms. Pac-Man 1982 Pac-Man Pac-Man Ms. Pac-Man Ms. Pac-Man Pac-Man Pac-Man Ms. Pac-Man Ms. Pac-Man Pac-Man Ms. Pac-Man Competition Ms. Pac-Man AI Competition Ms. Pac-Man CEC IEEE Congress on Evolusionary Computation 2008 WCCI IEEE World Congress on Computational Intelligence 2009 CIG IEEE COnference on Computational Intelligence and Games Ms. Pac-Man Competition 5 1 Ms. Pac-Man 10 2 Ms. Pac-Man 3 Ms. Pac-Man 4 15 FPS Ms. Pac-Man 5 Ms. Pac-Man Ms. Pac-Man Ms. Pac-Man Competition 3. Pac-Man Koza Genetic programming: on the programming of computers by means of natural selection TASKPRIORITIZATION Pac-Man 2) S S S Pac-Man 2000 AI
3 3819 Ms. Pac-Man Simon Ms. Pac-Man 3) 2000 Ms. Pac-Man Competition Ms. Pac- Man Ms. Pac-Man Competition Wirth Ms. Pac-Man 4) Robles Wirth 5) 40 Ms. Pac-Man Emilio 6) Ms. Pac-Man Tong 7) AI AI 4. Ms. Pac-Man 1 Ms. Pac-Man 5. UCT
4 3820 Ms. Pac-Man Coulom 8) Kocsis UCT UCB applied to Trees 9) UCT UCB1 UCB1 UCB Upper Confidence Bounds ln T X i + C (1) T i X i i T i i T C UCT UCT UCT UCT RTS Real Time Strategy 10),11) Ms. Pac-Man T C C Ms. Pac-Man C Ms. Pac-Man T C C C Ms. Pac-Man C 2 1
5 3821 Ms. Pac-Man 1 Ms. Pac-Man Fig. 1 A game tree for Ms. Pac-Man. UCT UCT UCB UCB i n (T i =0) UCB(i) = ln T X i + C T i (T i > 0) T i 0 n UCB UCB1 UCB UCB1 X i Ms. Pac-Man Ms. Pac-Man 1 P G (1) 8 1 (2) (3) P G C (4) P G (5) P (6) P (7) P G (8) G 2 (9) 1 G (10) 1 G (11) G (12) (2)
6 3822 Ms. Pac-Man C C 4 (1) C (2) C C (3) (1) (2) C C C (4) C Ms. Pac-Man P P (G type) =A(G type) AP RP AP AP RP A(G type) 1 C P (3) Table 1 1 Aggressiveness of the ghosts and their target points UCT Ms. Pac-Man
7 3823 Ms. Pac-Man 6.4 UCT A 30% B 40% B 3 C Ms. Pac-Man UCT 4.3 = Ms. Pac-Man Ms. Pac-Man Ms. Pac-Man 3 UCT UCT 1 UCT
8 3824 Ms. Pac-Man 2 Table 2 Selection of a movement path. 3 Table 3 Scores achieved. p min max mean s d 6,840 37,630 24, , ICE Pambush3 8,620 30,660 20, , UCT Ms. Pac-Man Ms. Pac-Man Competition 20 (1) (2) CPU Core2 Duo 3 GHz 2GB C Ms. Pac-Man Microsoft Games Revenge of Arcade 7.2 (1) C =2 grid =10 =2 (2) UCT =300 1 =30 (3) =0.20 = 120 pixel 7.3 ICE Pambush3 Ms. Pac-Man Competition CIG2009 ICE Pambush3 ICE Pambush3 Ruck THAWONMAS Ms. Pac-Man ICE Pambush ICE Pambush3 ICE Pambush3 37,630 Ms. Pac-Man Competition Ms. Pac-Man ICE Pacmbush3 ICE Pambush3 ICE Pambush ICE Pambush3 ICE Pambush3
9 3825 Ms. Pac-Man Table 4 4 Summarizes the number of stages reached. p min max mean s.d ICE Pambush Table 5 Scores achieved. p min max mean s d 6,840 37,630 24, , ,800 58,990 31, , ICE Pambush3 7.6 ICE Pambush3 UCT UCT Ms. Pac-Man UCT ICE Pambush X W i,j k (i, j) 1 n ppill > 0 n n k=1 X = Wi,j (n ppill =0) (4) n (n ppill > 0) ,990 Ms. Pac-Man
10 3826 Ms. Pac-Man Table 6 6 Summarizes the number of stages reached in the game. p min max mean s d ICE Pambsuh UCT Ms. Pac-Man Ms. Pac-Man Competition 1) Lucas, S.M.: Ms. Pac-Man Competition, available from 2) Koza, J.R.: Genetic Programming on the Programming of Computers by Means of Natural Selection, MIT Press (1992). 3) Lucas, S.M.: Evolving a Neural Network Location Evaluator to Play Ms. Pac- Man, IEEE Symposium on Computational Intelligence and Games 2005, pp (2005). 4) Wirth, N. and Gallagher, M.: An influence map model for playing Ms. Pac- Man, IEEE Symposium on Computational Intelligence and Games 2008, pp (2008). 5) Robles, D. and Lucas, S.: A Simple Tree Search Method for Playing Ms. Pac- Man, IEEE Symposium on Computational Intelligence and Games 2009, pp (2009). 6) Emilio, M., Moises, M., Gustavo, R. and Yago, S.: Pac-mAnt: Optimization Based on Ant Colonies Applied to Developing an Agent for Ms. Pac-Man, Proc IEEE Conference on Computational Intelligence and Games 2010, pp (2010). 7) Tong, B.K.B. and Sung, C.W.: A Monte-Carlo approach for ghost avoidance in the Ms. Pac-Man game, Proc. IEEE GIC, Hong Kong, pp.1 8 (Dec. 2010). 8) Coulom, R.: Efficient selectivity and backup operators in Monte-Calro tree search, Proc. 5th International Conference on Computer and Games (2006). 9) Kocsis, L. and Szepesvari, C.: Bandit based montecarlo planning, European Conference on Machine Learning, pp (2006). 10) Chung, M., Buro, M. and Schaeffer, J.: Monte Carlo Planning in RTS Games, IEEE Symposium on Computational Intelligence and Games (2005). 11) Balla, R. and Fern, A.: UCT for tactical assault planning in real-time strategy games, Proc. International Joint Conference on Artficial Intelligence 2009, pp (2009). 12) Matsumoto, H., Ashida, T., Ozasa, Y., Maruyama, T. and Thawonmas, R.: ICE Pambush 3, 2009 IEEE Symposium on Computational Intelligence and Games competition entry (2009). 13) Lucas, S.M.: Ms. Pac-Man Competition (screen capture mode) IEEE CIG 2011 Results, available from CIG2011Results.html Ms. Pac-Man Competition screen capture mode IEE CIG ,280 13)
11 3827 Ms. Pac-Man ( ) ( )
[1] AI [2] Pac-Man Ms. Pac-Man Ms. Pac-Man Pac-Man Ms. Pac-Man IEEE AI Ms. Pac-Man AI [3] AI 2011 UCT[4] [5] 58,990 Ms. Pac-Man AI Ms. Pac-Man 921,360
![[1] AI [2] Pac-Man Ms. Pac-Man Ms. Pac-Man Pac-Man Ms. Pac-Man IEEE AI Ms. Pac-Man AI [3] AI 2011 UCT[4] [5] 58,990 Ms. Pac-Man AI Ms. Pac-Man 921,360](/thumbs/93/114217704.jpg "[1] AI [2] Pac-Man Ms. Pac-Man Ms. Pac-Man Pac-Man Ms. Pac-Man IEEE AI Ms. Pac-Man AI [3] AI 2011 UCT[4] [5] 58,990 Ms. Pac-Man AI Ms. Pac-Man 921,360")
TD(λ) Ms. Pac-Man AI 1,a) 2 3 3 Ms. Pac-Man AI Ms. Pac-Man UCT (Upper Confidence Bounds applied to Trees) TD(λ) UCT UCT Progressive bias Progressive bias UCT UCT Ms. Pac-Man UCT Progressive bias TD(λ)
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