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Improving Tower Defense Game Ai (Genetic Algorithm And Genetic Programming)

Authors:Tio Chun Chieng and Chin Kim On

Abstract:

The processes of designing and developing in digital game is costly. Tower defense games received much attention recently. However, there are few challenges found in designing the maps used in the game; the maps used is either too easy to be played or too difficult to be won. It happened as programmers simply developed the maps without proper planning and testing. This research proposed a technique using Evolutionary Algorithms (EAs) and Artificial Neural Networks (ANNs) to auto generate controllers to test the proposed maps. The proposed method can lead to significantly better intelligent system rather than depending on either EAs or ANNs alone. The selected EAs are Genetic Algorithm (GA) and Genetic Programming (GP) and the selected ANNs are Feed-Forward Neural Network (FFNN), Elman Recurrent Neural Network (ERNN), Jordan Recurrent Neural Network (JRNN), and an Ensemble Neural Network (ENN). The proposed ENN is a weighted sum NN composed of single FFNN, single ERNN, and single JRNN. The elitism concept is integrated in the optimization processes.