Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A Neural-Evolutionary Model for Case-Based Planning in Real Time Strategy Games
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Lamarckian neuroevolution for visual control in the quake II environment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Backpropagation without human supervision for visual control in quake II
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Evolutionary neural networks for non-player characters in quake III
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Learning a context-aware weapon selection policy for unreal tournament III
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Case learning and indexing in real time strategy games
ICNC'09 Proceedings of the 5th international conference on Natural computation
Improving behavior of computer game bots using fictitious play
International Journal of Automation and Computing
Online behavior change detection in computer games
Expert Systems with Applications: An International Journal
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This paper presents a First Person Shooter Artificial Intelligence system that makes use of machine learning capabilities to achieve more human-like behavior and strategies. The AI is trained with a supervised learning paradigm using example recorded during the observation of expert human players. The Machine Learning section of the AI is based on various Feed Forward Multi-layer Neural Networks trained by Genetic Algorithms. The AI system is developed and tested in the Quake 3 Arena game engine. The system is able to learn certain behaviors but still lack on some others. The results are evaluated and possible improvements are proposed.