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http://dx.doi.org/10.3745/KIPSTB.2008.15-B.3.253

Game Agent Learning with Genetic Programming in Pursuit-Evasion Problem  

Kwon, O-Kyang (성균관대학교 컴퓨터공학부)
Park, Jong-Koo (성균관대학교 정보통신공학부)
Publication Information
The KIPS Transactions:PartB / v.15B, no.3, 2008 , pp. 253-258 More about this Journal
Abstract
Recently, game players want new game requiring more various tactics and strategies in the complex environment beyond simple and repetitive play. Various artificial intelligence techniques have been suggested to make the game characters learn within this environment, and the recent researches include the neural network and the genetic algorithm. The Genetic programming(GP) has been used in this study for learning strategy of the agent in the pursuit-evasion problem which is used widely in the game theories. The suggested GP algorithm is faster than the existing algorithm such as neural network, it can be understood instinctively, and it has high adaptability since the evolving chromosomes can be transformed to the reasoning rules.
Keywords
Artificial Intelligence; Decision Tree; Genetic Programming; Pursuit-Evasion Problem;
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