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Improving Tower Defense Game Ai (Differential Evolution Vs Evolutionary Programming)

Authors: Cheah Keei Yuan and Chin Kim On

Abstract:

The use of Artificial Intelligence has emerged into every corner of our daily life. In this modern technology era, there are many 3-Dimensional games are using AI methods to bring out a better gaming experience. The most used of AI in gaming environment is Real Time Strategy games which Real Time means actual time during a process whereas Strategy means a set of different skills. Tower Defense games are one of the Real Time Strategy category which human players exert their gameplay strategy to build tower and win highest level of game. Research on implementing Artificial Intelligence to Tower Defense games are seems unpopular in the world but Tower Defense games have been proven that its simplicity and availability to create a test bed for this research. In this research, two proposed Evolutionary Algorithms comprising of Differential Evolution and Evolutionary Programming to evolve weight of Jordan Recurrent Neural Network, Elman’s Recurrent Neural Network and Feed Forward Neural Network and Ensemble Neural Network. Ensemble Neural Network ensembles Jordan Recurrent Neural Network, Elman’s Recurrent Neural Network and Feed Forward Neural Network to compare robustness and performance. There are 10 runs for each experiments of total of 8 algorithms used to get an average results and make comparison, using average archive best fitness and winning rate. The result shows, the performance for Evolutionary Algorithms, Differential Evolution has better performance than Evolutionary Programming, as the performance for Artificial Neural Network, Ensemble Neural Network proves to have slightly better than other neural network. The best controller in this research is Differential Evolution evolving Ensemble Neural Network which has highest average archive fitness score and winning rate.

 

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