Self-synthesized controllers for tower defense game using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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