Self-synthesized controllers for tower defense game using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Leong:2013:ICCSCE,

• author = "Leow Chin Leong and Gan Kim Soon and Tan Tse Guan and Chin Kim On and Rayner Alfred and Patricia Anthony",
• title = "Self-synthesized controllers for tower defense game using genetic programming",
• booktitle = "IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2013)",
• year = "2013",
• month = nov,
• pages = "487--492",
• keywords = "genetic algorithms, genetic programming, artificial intelligence, computer games, Artificial Neural Network, ANN, Tower Defence (TD) Game, Feed-forward Neural Network, FFNN, Elman-Recurrent Neural Network, ERNN",
• DOI = "doi:10.1109/ICCSCE.2013.6720014",
• abstract = "In this paper, we describe the results of implementing Genetic Programming (GP) using two different Artificial Neural Networks (ANN) topologies in a customised Tower Defence (TD) games. The ANNs used are (1) Feed-forward Neural Network (FFNN) and (2) Elman-Recurrent Neural Network (ERNN). TD game is one of the strategy game genres. Players are required to build towers in order to prevent the creeps from reaching their bases. Lives will be deducted if any creeps manage to reach the base. In this research, a map will be designed. The AI method used will self-synthesise and analyse the level of difficulty of the designed map. The GP acts as a tuner of the weights in ANNs. The ANNs will act as players to block the creeps from reaching the base. The map will then be evaluated by the ANNs in the testing phase. Our findings showed that GP works well with ERNN compared to GP with FFNN.",
• notes = "Also known as \cite{6720014}",

}

Genetic Programming entries for Leow Chin Leong Gan Kim Soon Tan Tse Guan Chin Kim On Rayner Alfred Patricia Anthony

Citations