By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

7 Author(s)
Xiao Liu ; Int. Sch., Beijing Univ. of Posts & Telecommun., Beijing, China ; Yao Li ; Suoju He ; Yiwen Fu
more authors

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.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:5 )

Date of Conference:

14-16 Aug. 2009