BROWSE
Detail
Conference Paper
Comparison of AI Techniques for Fighting Action Games - Genetic Algorithms/Neural Networks/Evolutionary Neural Networks
Cited 6 time in 
Share 
Share
- Authors
- Byeong Heon Cho, Chang Joon Park, Kwang Ho Yang
- Issue Date
- 2007-09
- Citation
- International Conference of Entertainment Computing (ICEC) 2007 (LNCS 4740), v.4740, pp.55-65
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- Abstract
- Recently many studies have attempted to implement intelligent characters for fighting action games. They used genetic algorithms, neural networks, and evolutionary neural networks to create intelligent characters. This study quantitatively compared the performance of these three AI techniques in the same game and experimental environments, and analyzed the results of experiments. As a result, neural network and evolutionary neural network showed excellent performance in the final convergence score ratio while evolutionary neural network and genetic algorithms showed excellent performance in convergence speed. In conclusion, evolutionary neural network which showed excellent results in both the final convergence score ratio and the convergence score is most appropriate AI technique for fighting action games. © IFIP International Federation for Information Processing 2007.
- KSP Keywords
- AI techniques, Action Games, Evolutionary neural networks, Genetic Algorithm, International federation, Score ratio, convergence speed, excellent performance, information processing