NDLI: To Create Intelligent Adaptive Game Opponent by Using Monte-Carlo for the Game of Pac-Man

Content Provider	IEEE Xplore Digital Library
Author	Xiao Liu Yao Li Suoju He Yiwen Fu Jiajian Yang Donglin Ji Yang Chen
Copyright Year	2009
Abstract	Adaptive Game AI improves adaptability of opponent AI with the challenge level of the gameplay; as a result the entertainment of game is augmented. Opponent game AI is usually implemented by scripted rules in video games. However MCT (Monte-Carlo for Trees), a most updated algorithm which perform excellent in computer go can also be used to achieve excellent result to control non-player characters (NPCs) in video games. In this paper, the prey and predator game genre of Pac-Man is used as a test-bed, the basic principle of MCT is presented, and the effectiveness of its application to game AI development is demonstrated. Furthermore, in order to reduce the computation intensiveness of Monte-Carlo, ANN (Artificial Neural Network) is used to produce the intelligence of game opponent with the data collected from Monte-Carlo method. The effectiveness and efficiency of the process is proved.
Starting Page	598
Ending Page	602
File Size	944663
Page Count	5
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.633
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Keywords: Adaptive Game AI Artificial neural networks Monte-Carlo Telecommunication computing Helium Application software Intelligent networks Games Computer networks Artificial intelligence Pac-Man Testing MCT
Content Type	Text
Resource Type	Article

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