Meili Zhu
ABSTRACT
In real game scenes, there may be a problem that it is impossible to collect a large amount of data to train NPC's correct behavior. This paper implements a method of training the neural networks to control game NPC behavior based on an improved genetic algorithm. This method optimizes the weights of the fixed network structure through the genetic algorithm, realizes the self-evolution of the neural network, and improves the fitness function and the selection and crossover mutation method in the traditional genetic algorithm, so as to be suitable for game production. Testing in two real game scenes shows that game NPC can acquire intelligent behavior capabilities using this algorithm.
- Liu Wei, Wang Muxuan. 2016, Talking about artificial intelligence and game thinking. Science and Society, 6(3):18.Google Scholar
- Zhu Daqi, Shi Hui, 2006. "The Principle and Application of Artificial Neural Networks" . Science Press..Google Scholar
- Funge J D.2004. Artificial intelligence for computer games: an introduction[M]. New York: PETERS A K, Ltd.Google Scholar
- Holland J H. 1992, Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence. Quarterly Review of Biology, 6(2): 126–137.Google Scholar
- D Silver, A Huang, CJ Maddison, A Guez, LSifre. 2016 Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587):484-489.Google ScholarCross Ref
- Miikkulainen R, Liang J, Meyerson E, 2019. Evolving deep neural networks. Artificial Intelligence in the Age of Neural Networks and Brain Computing. Academic Press, 293-312.Google Scholar
- Ding S, Zhang Y, Chen J, et al. 2013. Research on using genetic algorithms to optimize Elman neural networks. Neural Computing and Applications.23)2(:293-297.Google Scholar
- Liang J, Meyerson E, Hodjat B, 2019. Evolutionary neural Auto-ML for deep learning. Genetic and Evolutionary Computation Conference, 401-409.Google ScholarDigital Library
- S. Yang, Y. Tian, C. He, 2021. A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 3061630.Google Scholar
- Wu Yafeng. 2015. Unity Game Case Development Encyclopedia. Beijing: People's Posts and Telecommunications Press.Google Scholar
Index Terms
- Design and Implementation of NPC AI based on Genetic Algorithm and BP Neural Network
Recommendations
An optimizing BP neural network algorithm based on genetic algorithm
A back-propagation (BP) neural network has good self-learning, self-adapting and generalization ability, but it may easily get stuck in a local minimum, and has a poor rate of convergence. Therefore, a method to optimize a BP algorithm based on a ...
Application of BP Neural Network Based on Genetic Algorithm Optimization
ICIIP '19: Proceedings of the 4th International Conference on Intelligent Information ProcessingIn order to improve the accuracy of the BP neural network prediction model to predict the transmission synchronizer shift fault, a BP neural network prediction method based on genetic algorithm optimization is proposed. The characteristics and defects ...
The Research of Alphabet Identification Based on Genetic BP Neural Network
IHMSC '12: Proceedings of the 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 01The Back Propagation (BP) neural network genetic algorithm was used to identify alphabet, and the new algorithm combine the advantages of both genetic algorithm and the BP neural network. Genetic learning algorithm was used for the global optimization ...
Comments