Abstract
This paper presents the research result of implementing evolutionary algorithms towards computational intelligence in Tower Defense game (TD game). TD game is a game where player(s) need to build tower to prevent the creeps from reaching their based. Penalty will be given if player losses any creeps during gameplays. It is a suitable test bed for planning, designing, implementing and testing either new or modified AI techniques due to the complexity and dynamicity of the game. In this research, Genetic Algorithm (GA) will be implemented to the game with two different neural networks: (1) Feed- forward (FFNN) and (2) Elman Recurrent (ERNN) used as tuner of the weights. ANN will determine the placement of the towers and the fitness score will be calculated at the end of each game. As a result, it is proven that the implementation of GA towards FFNN is better compared to GA towards ERNN.
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References
Avery, P., Togelius, J., Alistar, E.: Computational Intelligence and Tower Defence Games. In: IEEE Congress on Evolutionary Comp. (CEC), pp. 1084–1091 (2011)
Plants vs. Zombies - Walkthrough/guide, http://www.ign.com/faqs/2009/plants-vs-zombies-walkthroughguide-992681
Fieldrunners 2 – An Absolute Masterpiece in Every Regard, http://applenapps.com/review/fieldrunners-2-an-absolute-masterpiece-in-every-regard
‘Sentinel Earth 2 – Earth Defense’ – Does It Live Up to the Original?, http://toucharcade.com/2009/07/08/sentinel-2-earth-defense-does-it-live-up-to-the-original/
Rummell, P.A.: Adaptive AI to Play Tower Defense Game. In: The 16th International Conference on Computer Games, CGAMES, pp. 38–40. IEEE (2011)
Chang, K.T., Chin, K.O., Teo, J., James, M.: Game AI Generation using Evolutionary Multi-Objective Optimization. In: Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)
Chang, K.T., Chin, K.O., Teo, J., Chua, B.L.: Automatic Generation of Real Time Strategy Tournament Units using Differential Evolution. In: Proceedings of the IEEE Conference on Sustainable Utilization and Development in Engineering and Technology, pp. 101–106. IEEE (2011)
Ng, C.H., Niew, S.H., Chin, K.O., Teo, J.: Infinite Mario Bross AI using genetic algorithm. In: IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (2011 IEEE STUDENT). The University of Nottingham, Malaysia Campus (2011)
Chang, K.T., Ong, J.H., Teo, J., Chin, K.O.: The Evolution of Gamebots for 3D First Person Shooter (FPS). In: IEEE the Sixth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2011). Universiti Sains Malaysia, Penang (2011)
Bourg, D.M., Seeman, G.: AI for Game Developers. O’Reilly (2004)
Moriarty, D., Miikkulainen, R.: Discovering Complex Othello Strategies Through Evolutionary Neural Networks. Connection Science 7, 195–209 (1995)
Chellapilla, K., Fogel, D.B.: Evolving Neural Networks to Play Checkers without Relying on Expert Knowledge. IEEE Transactions on Neural Networks 10(6), 1382–1391 (1999)
Fogel, D.B.: Using Evolutionary Programming to Create Neural Networks that are Capable of Playing Tic-Tac-Toe. In: IEEE International Conference on Neural Networks (ICNN), vol. 2, pp. 875–880 (1993)
Freisleben, B.: A Neural Network that Learns to Play Five-in-a-row. In: Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, pp. 20–23 (1995)
Wong, S.K., Fang, S.W.: A Study on Genetic Algorithm and Neural Network for Mini-Games. Journal of Information Science and Engineering 28, 145–159 (2012)
Niu, B., Wang, H., Ng, P.H.F., Shiu, S.C.K.: A Neural-Evolutionary Model for Case-Based Planning in Real Time Strategy Games. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS(LNAI), vol. 5579, pp. 291–300. Springer, Heidelberg (2009)
Wang, H., Ng, P.H.F., Niue, B., Shiu, S.C.K.: Case Learning and Indexing in Real Time Strategy Games. In: Fifth International Conference on Natural Computation, pp. 100–104. IEEE (2009)
Krenker, A., Bester, J., Kos, A.: Introduction to the Artificial Neural Networks. In: Artificial Neural Networks – Methodology Advances and Biomedical Applications. InTech, pp. 3–18 (2011)
McCulloch, W., Pitts, W.: A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)
Carlos, G.: Artificial Neural Networks for Beginners. In: Artificial Neural Networks – Methodology Advances and Biomedical Applications. InTech (2011)
Xiang, W.J., Liu, H., Sun, Y.H., Su, X.N.: Application of Genetic Algorithm in Document Clustering. In: International Conference on Information Technology and Computer Science, pp. 145–148. IEEE (2009)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press (1999)
Sivaraj, R.: A Review of Selection Methods in Genetic Algorithm. International Journal of Engineering Science and Technology (IJEST) 3(5), 3792–3797 (2011)
Talib, S.H.: An Introduction to Evolutionary Computation (1998)
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Tan, T.G., Yong, Y.N., Chin, K.O., Teo, J., Alfred, R. (2013). Automated Evaluation for AI Controllers in Tower Defense Game Using Genetic Algorithm. In: Noah, S.A., et al. Soft Computing Applications and Intelligent Systems. M-CAIT 2013. Communications in Computer and Information Science, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40567-9_12
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DOI: https://doi.org/10.1007/978-3-642-40567-9_12
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