GAME PLAYER STRATEGY PATTERN RECOGNITION BY USING K-NEAREST NEIGHBOR

被引：0

作者：

He, Suoju ^{[1
]}

Du, Junping ^{[1
]}

Wu, Guoshi ^{[1
]}

Li, Jing ^{[1
]}

Wang, Yi ^{[1
]}

Xie, Fan ^{[1
]}

Liu, Zhiqing ^{[1
]}

Zhu, Qiliang ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China

来源：

CIICT 2008: PROCEEDINGS OF CHINA-IRELAND INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATIONS TECHNOLOGIES 2008 | 2008年

关键词：

Player Strategy; Pattern Recognition; KNN; Pac-Man;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Pattern recognition has been successfully used in different application areas, its application on identifying player's strategy during the gameplay which is called Player Strategy Pattern Recognition (PSPR), is another interesting area. PSPR can greatly improve game AI's adaptability, and as a result the entertainment of game is promoted. In this paper, Pac-Man game is used as a test-bed. Classifier of k-nearest neighbor (KNN) algorithm is chosen to analyze off-line data from gamers who are choosing different strategies, in other words the classifiers are trained with sample data from players using different strategies. The method attempts to use the trained classifier to predict strategy pattern of a future player based on the data captured from its gameplay. This paper presents the basic principle of the PSPR by using the KNN theoretic approach and discusses the results of the experiments.

引用

页码：190 / 193

页数：4

共 50 条

[31] k-Nearest Neighbor Classification Using Dissimilarity Increments
Aidos, Helena
Fred, Ana
IMAGE ANALYSIS AND RECOGNITION, PT I, 2012, 7324 : 27 - 33
[32] Optical character recognition in real environments using neural networks and k-nearest neighbor
O. Matei
P. C. Pop
H. Vălean
Applied Intelligence, 2013, 39 : 739 - 748
[33] Diagnosis of Arthritis Using K-Nearest Neighbor Approach
Kaur, Rupinder
Madaan, Vishu
Agrawal, Prateek
ADVANCED INFORMATICS FOR COMPUTING RESEARCH, PT I, 2019, 1075 : 160 - 171
[34] Emotion recognition from multichannel EEG signals using K-nearest neighbor classification
Li, Mi
Xu, Hongpei
Liu, Xingwang
Lu, Shengfu
TECHNOLOGY AND HEALTH CARE, 2018, 26 : S509 - S519
[35] Optical character recognition in real environments using neural networks and k-nearest neighbor
Matei, O.
Pop, P. C.
Valean, H.
APPLIED INTELLIGENCE, 2013, 39 (04) : 739 - 748
[36] A new edited k-nearest neighbor rule in the pattern classification problem
Hattori, K
Takahashi, M
PATTERN RECOGNITION, 2000, 33 (03) : 521 - 528
[37] MKNN: Modified K-Nearest Neighbor
Parvin, Hamid
Alizadeh, Hoscin
Minael-Bidgoli, Behrouz
WCECS 2008: WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, 2008, : 831 - 834
[38] A GENERALIZED K-NEAREST NEIGHBOR RULE
PATRICK, EA
FISCHER, FP
INFORMATION AND CONTROL, 1970, 16 (02): : 128 - &
[39] Improved k-nearest neighbor classification
Wu, YQ
Ianakiev, K
Govindaraju, V
PATTERN RECOGNITION, 2002, 35 (10) : 2311 - 2318
[40] Navigating K-Nearest Neighbor Graphs to Solve Nearest Neighbor Searches
Chavez, Edgar
Sadit Tellez, Eric
ADVANCES IN PATTERN RECOGNITION, 2010, 6256 : 270 - 280

← 1 2 3 4 5 →