Gobang game algorithm based on LabVIEW

Journal of Computer Applications,2016,36( 6) : 1630-1633,1638 ISSN 1001-9081 CODEN JYIIDU 2016-06-10 http: / /www. joca. cn 1001-9081 2016 06-1630-04 DOI 10. 11772 /j. issn. 1001-9081. 2016. 06. 1630 LabVIEW 1 *, 1, 1,2 1, 1. 215500 2. 110014 * maolimin_1981@ 163. com, LabVIEW, ; ;,,, LabVIEW ; ; LabVIEW; ; TP242. 6 A Gobang game algorithm based on LabVIEW MAO Limin 1* ZHU Peiyi 1 LU Zhenli 1 2 PENG Weiwei 1 1. School of Electric and Automatic Engineering Changshu Institute of Technology Changshu Jiangsu 215500 China 2. State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning 110014 China Abstract The current researches of Gobang man-machine game are mostly based on the computer mobile phone which are lacking real environments. In order to solve the problem a game algorithm based on Laboratory Virtual Instrument Engineering Workbench LabVIEW was proposed and was applied to Gobang man-machine game in real environment. Firstly the state information of the chess board and the man-machine chess pieces location on both sides in the current state were obtained by the image acquisition system. Then the game situation was analyzed. In order to improve the efficiency of chess the chess type was classified and the original game algorithm was improved by using two weights of attack and defense to simplify the decision-making process. The experimental results of real game tests prove that the proposed algorithm based on LabVIEW can realize the Gobang man-machine chess fast and accurately. robot 0 Key words Gobang game Laboratory Virtual Instrument Engineering Workbench LabVIEW man-machine game International Federation of Robotics IFR 2017 800 21 3 LabVIEW 1 Laboratory Virtual Instrument 1 Engineering Workbench LabVIEW 1. 1 2 LabVIEW 15 225 National Instruments NI Vision 4 Builder NI Vision Builder IMAQ Vision IMAQ Vision A ~ O 1 ~ 15 1 2015-10-29 2016-02-01 SYG201504 1981 1980 CCF 1974 CCF 1993

6 : LabVIEW 1631 8 H8 8H 1 4 1 2 1 1. 2 1 Rule Interchange Format RIF 4 3 2 1 4 3 2 H8 1 1 2 1 6 2 2 2. 1 15* 15 3 15* 15 0 1 2 2. 2 1 3 1 2 4 2. 3 1

1632 36 8 8 1 AI Artificial Intelligence 5 6 5 6 AI 2. 4 1 9 2 7 A 1 A 2 A 3 3 A 4 1 2 3 4 F n = n n Q i A i 1 i = 1 1 1 20 0 0 0 Q i 2 3 600 0 0 0 3 0 0 1 30 2 300 10 4 0 1 2 0 1 2 5 0 3 300 1 40 1 20 6 0 0 1 26 0 7 0 0 2 300 0 value 2 4 4 8 0 0 0 0 4 630 312 225 6 337 A i B i 3 2 2 32 11 F n = n Q i A i + P i B i 2 i = 1 3 Q i P i 9 2 1 2 3 4 40 400 300 10 000 6 10 600 10 000 20 120 200 0 6 10 500 0 30 300 2 500 5 000 2 8 300 8 000 26 160 0 0 4 20 300 0 H8 I9 7 AI 3 3 3 8

6 : LabVIEW 1633 13 e F9 13 f 13 g 13 h 13 i J6 I6 I8 J8 13 g 8 12 9 2. 5 5 12 10 11 4 13 10 11 3 2010 10 2 45-47. 3. 2010 H8 12 13 13 b H6 LabVIEW J 13 c I7 205. 13 d G9 LabVIEW 1. 2013 39 7 963-972. TAN M WANG S. Research progress on robotics J. Acta Automatica Sinica 2013 39 7 963-972. 2. LabVIEW J. 2010 10 2 45-47. LIU C Y WU D H. A research on vehicle license plate image recognition technology based on LabVIEW and its application Journal of Langfang Teachers College Natural Science Edition LabVIEW 26 10-2 204-205. DING S JIANG X Y WANG X. Study on the image processing technology based on. Control and Automation 2010 26 10-2 204 - ( 1638 )

1638 36 matics 2001 15 1 89-92. 4 DUDOIT S FRIDLYAND J. A prediction-based resampling method for estimating the number of clusters in a dataset J. Genome Biology 2002 3 7 1-21. 5 HALKIDI M BATISTAKIS Y VAZIRGIANNIS M. Clustering validity checking methods part II ACM SIGMOD Record 2002 31 3 19-27. 6. 14 WANG W ZHANG Y. On fuzzy cluster validity indices J. 2002 24 8 1017-1021. FAN J Fuzzy Sets and Systems 2007 158 19 2095-2117. L WU C M. Clustering validity function based on possibilistic parti- 15 REZAEE B. A cluster validity index for fuzzy clustering tion coefficient combined with fuzzy variation Journal of Electronics and Information Technology 2002 24 8 1017-1021. 16 ALEX R ALESSANDRO L. Machine learning. Clustering by Fuzzy Sets and Systems 2010 161 23 3014-3025. fast 7 YU J CHENG G. Search range of the optimal number of clusters in fuzzy clustering J. Science in China Series E 2002 32 2 274-280. 8 SUN H WANG S JIANG Q. FCM-based model selection algorithms for determining the number of clusters Pattern Recognition 2004 37 10 2027-2037. 9 BOUGUESSA M WANG S SUN H. An objective approach to cluster validation Pattern Recognition Letters 2006 27 13 1419-1430. 10. 2008 19 1 48-61. SUN J G LIU J ZHAO L Y. Clustering algorithms research J. Journal of Software 2008 19 1 48-61. 11 CELEBI M E KINGRAVI H A VELA P A. A comparative study of efficient initialization methods for the k-means clustering algorithm J. Expert Systems with Applications 2013 40 1 200-210. J. 2008 19 1 62-72. CHEN L F JIANG Q S WANG S R. A hierarchical method for determining the number of clusters J. Journal of Software 2008 19 1 62-72. 13 PAKHIRA M K BANDYOPADHYAY S MAULIK U. A study of some fuzzy cluster validity indices genetic clustering and application to pixel classification Fuzzy Sets and Systems 2005 155 2 191-214. search and find of density peaks Science 2014 344 6191 1492-1496. 17 AGRAWAL R GEHRKE J GUNOPULOS D et al. Automatic subspace clustering of high dimensional data J. Data Mining & Knowledge Discovery 2005 11 1 5-33. 18 MEDEIROS C M S BARRETO G A. A novel weight pruning method for MLP classifiers based on the MAXCORE principle J. Neural Computing & Applications 2013 22 1 71-84. Background This work is partially supported by the National Natural Science Foundation of China 61572301 90612003 the Shandong Provincial Natural Science Foundation ZR2013FM008. PANG Lin born in 1991 M. S. candidate. Her research interests include data mining big data analysis. LIU Fang ai born in 1962 Ph. D. professor. His research inter- 12. ests include wireless network distributed computation. ( 1633 ) 4. 10. J. 2012 32 7 1969-1972 1990. ZHANG M 2007 28 L WU J LI F C. Design of evaluation-function for computer gobang game system J. Journal of Computer Applications 2012 32 and manipulator s chess-playing system based on image processing 1 73-75 93. JIN Y Y LI X LIU G J. Development of man 7 1969-1972 1990. Journal of Qingdao University of Science and Technology 5. Natural Science Edition 2007 28 1 73-75 93. 2006 25 2 71-74. ZHU Q M CHEN S Q. Gobang al- 11. gorithm research and think Computing Technology and Automation 2006 25 2 71-74. J. 2004 31 6 50-52. HUANG L B 6. 2014 28 4 99-103. ZHANG X C HOU X L TU F. Behavior planning for the game robot J. Journal of Chongqing University of Technology Natural Science Edition 2014 28 4 99-103. 7 2004 40 1 74-76. JIANG J F CHEN A X TANG X Y. Search algorithm for game of checkers based on knowledge inference J. Computer Engineering and Applications R M. Study on valuation algorithm and game training in computer. game J. Computer Engineering 2012 38 11 163-166. J. Science Technology and Engineering 2012 12 5 1052-1055 1060.. XIA T K WANG C X et al. Design and analysis of the Chinesechess robot in real time environment Machinery 2004 31 6 50-52. 12. J. 2012 38 11 163-166. LYU Y H GONG Background 2004 40 1 74-76. This work is partially supported by the Suzhou Science and Technolo- 8. gy Project SYG201504. 2012 12 5 1052-1055 1060. YANG Y Q MAO Limin born in 1981 M. S. lecturer. His research interests WU J. An Improved Gobang system based on game-playing tree include robot control target tracking. ZHU Peiyi born in 1980 Ph. D. lecturer. His research interests include intelligent control data fusion. 9. LU Zhenli born in 1974 Ph. D. lecturer. His research interests 2004 27 7 25-27. ZHANG H F BAI Z X ZHANG include intelligent control machine vision. D F. Design of playgame intelligence in Gobang J. Modern Electronics Technique 2004 27 7 25-27. clude data acquisition signal PENG Weiwei born in 1993 engineer. His research interests in- processing.