To Create Adaptive Game Opponent by Using UCT

Adaptive game AI improves adaptability of opponent AI as well as 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, but the most updated algorithm of UCT (upper confidence bound for trees)...

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Bibliographic Details
Main Authors: Suoju He, Fan Xie, Yi Wang, Sai Luo, Yiwen Fu, Jiajian Yang, Zhiqing Liu, Qiliang Zhu
Format: Conference Proceeding
Language:English
Subjects:
Adaptive Game AI
Application software
Artificial intelligence
Dead End
Discrete event simulation
Dogs
Games
Helium
Machine learning algorithms
Pattern recognition
Software engineering
Testing
UCT
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Summary:Adaptive game AI improves adaptability of opponent AI as well as 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, but the most updated algorithm of UCT (upper confidence bound for trees) 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 Dead End is used as a test-bed, the basic principle of UCT is presented, and the effectiveness of its application to game AI development is demonstrated. The experiment compares the performance of different NPCs control approaches: given a 300 milliseconds for each simulation step, the approach of UCT with recognized playerpsilas strategy pattern is better than the one of UCT without recognized playerpsilas strategy pattern, the worst one is Monte-Carlo approach.
DOI:10.1109/CIMCA.2008.81