RoboCup 1 2D 3D Figre 1 2 2D 3D 2D 2D 3D 2D 2D Earth Mover s Distance Earth Mover s Distance 3.1 (x y ) p i w pi Figure 3 opuscom Uv

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社団法人人工知能学会 Japanese Society for Artificial Intelligence 人工知能学会研究会資料 JSAI Technical Report SIG-Challenge-042-01 (5/3) RoboCup Predicting Game Results using Kick Distributions in RoboCup,, Jordan Henrio,, Satoshi Mifune, Tomoharu Nakashima, Jordan Henrio, Hidehisa Akiyama Soichi Ayai Osaka prefecture University, Fukuoka University satoshi.mifune@cs.osakafu-u.ac.jp, tomoharu.nakashima@kis.osakafu-u.ac.jp jordan.henrio@cs.osakafu-u.ac.jp, akym@fukuoka-u.ac.jp, swa01014@edu.osakafu-u.ac.jp Abstract Predicting the game result using kick distributions is studied in this paper. Although it is not possible to exactly know a strategy that a team is taking, that strategy might be well represented by how the players in the team kick the ball during games. Passes and dribbles that are made during a game are extracted to form a kick distribution. It is assumed that the kick distribution represents the strategy of a team. A series of computational experiments are conducted in order to examine the performance of the proposed method. 1 RoboCup[1] RoboCup Gabel [2] Abreu [3] 1 RoboCup 2D Earth Mover s Distance (EMD) [4] 4 2 RoboCup RoboCup RoboCup 2050 RoboCup RoboCup 1

RoboCup 1 2D 3D Figre 1 2 2D 3D 2D 2D 3D 2D 2D 3000 6000 1 0.1 3 Earth Mover s Distance Earth Mover s Distance 3.1 (x y ) p i w pi Figure 3 opuscom UvA Trilearn opuscom Figure 3 35 30 25 20 15 10 5 1: 2D Simulation League 40 20 0 20 30 20 10 0 10 20 40 30 0 3: kick distribution that is obtained from a game between opuscom and UvA Trilearn 3.2 Earth Mover s Distance 2: 3D Simulation League Earth Movers s Distance(EMD) EMD [5] EMD 2

EMD P P = {(p 1, w p1 ),, (p m, w pm )} P m p i w pi Q Q = {(q 1, w q1 ),, (q m, w qm )} EMD 2 p i q j d ij D = [d ij ] p i q j f ij F = [f ij ] (1) F EMD W = d ij f ij (1) f ij 0 (1 i m, 1 j n) (2) n f ij w pi (1 i m) (3) j=1 m f ij w qi (1 j m) (4) i=1 f ij = min( w pi, w qj ) (5) (2) (3) p i (4) q i (5) F P Q EMD d ij fij EMD(P, Q) = m n f ij (6) W EMD 3.3 Step 1 : EMD Step 2 : Step 3 : Step 4 : 2 Step 5 : 1 2 Step 3 3.4 3.3 4 4.1 UvA Trilearn(2005)[6] BrainStomers(2009)[7] HELIOS(2014)[8] WrightEagle(2014)[9] 4 opuscom(2014) 10 4 40 opuscom x y EMD EMD 3

4.2 5 opuscom 2 UvA Trilearn UvA Trilearn 5.3 opuscom opuscom opuscom Figure 5 5.1 UvA Trilearn(2005) BrainStomers(2009) HE- LIOS(2014) WrightEagle(2014) 4 opus- COM(2014) 10 40 UvA Trilearn BrainStomers HELIOS WrightEagle 4 opuscom Table 1 1: Game results with UvA Trilearn, BrainStormers, HELIOS and WrightEagle Win Draw Lose UvA Trilearn 10 0 0 BrainStomers 7 1 2 WrightEagle 0 0 10 HELIOS 0 0 10 5.2 Figure 4 5: Clustering result for kick distributions of opus- COM Figure 5 U UvA Trilearn B Brain- Stomers W WrightEagle H HELIOS opuscom opuscom 3 5.4 4: Clustering result for kick distributions of opponents Figure 4 U UvA Trilearn B Brain- Stomers W WrightEagle H HELIOS 1 Figure 6 Figure 6 U UvA Trilearn B Brain- Stomers W WrightEagle H HELIOS opuscom 2 4

6: Clustering result for kick distributions of opus- COM and opponents 7: Labels of clusters : opponent team s kick distribution 5.5 UvA Trilearn BrainStomers HE- LIOS WrightEagle 4 opuscom Table 2 2: Game results with UvA Trilearn, BrainStormers, HELIOS and WrightEagle Win Draw Lose UvA Trilearn 9 0 1 BrainStomers 10 0 0 WrightEagle 1 0 9 HELIOS 0 0 10 8: Labels of clusters : our team s kick distribution opuscom Figure 7 8 9 Table 3 3: The number of correct opuscom UvA Trilearn 4 10 10 BrainStomers 1 8 4 WrightEagle 9 9 9 HELIOS 10 0 10 6 6.75 8.25 9: Labels of clusters : both team s kick distribution 5

Table 3 opuscom UvA Trilearn 6 EMD [6] Julle R. Kok and Nikos Vlassis, UvA Trilearn2005 Team Description Paper, RoboCup2005, CD-ROM (5 pages), Osaka, Japan(2005). [7] Thomas Gabel, Martin Riedmiller, BrainStormers 2D - Team Description 2009, RoboCup2009, CD- ROM (6 pages), Graz, Austria(2009). [8] Hidehisa Akiyama, Tomoharu Nakashima, Katsuhiro Yamashita, Satoshi Mifune, HELIOS2014 Team Description Paper, RoboCup2014, CD-ROM (6 pages), JoãoPessoa, Brazil(2014). [9] Haochong Zhang, Guanghui Lu, Rongya Chen, Xiao Li and Xiaoping Chen, WrightEagle 2D Soccer Simulation Team Description 2014, RoboCup2014, CD-ROM (6 pages), JoãoPessoa, Brazil(2014). [1] Hiroaki Kitano, Minoru Asada, Yasuo Kuniyoshi, Itsuki Noda, Eiichi Osawa and Hitoshi Matsubara, RoboCup: A Challenge Problem for AI, AIM agazine, Vol.18, No.1, pp.73-85(1997). [2] Thomas Gabel, Martin Riedmiller On Progress in RoboCup: The Simulation League Showcase The 14th RoboCup 2010 Symposium pp.36-47 Springer Berlin Heidelberg(2010). [3] Pedro Abreu, João Moreira, Israel Costa, Daniel Castelão, Luis Reis, Júlio Garganta, Human Versus Virtual Robotics Soccer: A Technical Analysis, European Journal of Sport Science 12(1), pp.26-35, Taylor & Francis(2011) [4] Y.Rubner, C.Tomasi and L.J.guibas, The earth mover s distance as a metric for image retrieval, International Journal of Computer Vision, 40(2), pp.99-121(2000) [5] Earth Mover s Distance (2007). 6

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