Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An integrated environment for knowledge acquisition
Proceedings of the 6th international conference on Intelligent user interfaces
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
AI and the Entertainment Industry
IEEE Intelligent Systems
AI Game Programming Wisdom, Vol. 2
AI Game Programming Wisdom, Vol. 2
Queue - Game Development
Object-Oriented Game Development
Object-Oriented Game Development
Adaptive game AI with dynamic scripting
Machine Learning
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Game artificial intelligence (AI) controls the decision-making process of computer-controlled opponents in computer games. Adaptive game AI (i.e., game AI that can automatically adapt the behaviour of the computer players to changes in the environment) can increase the entertainment value of computer games. Successful adaptive game AI is invariably based on the game's domain knowledge. We show that an offline evolutionary algorithm can learn important domain knowledge in the form of game tactics (i.e., a sequence of game actions) for dynamic scripting, an offline algorithm inspired by reinforcement learning approaches that we use to create adaptive game AI. We compare the performance of dynamic scripting under three conditions for defeating non-adaptive opponents in a real-time strategy game. In the first condition, we manually encode its tactics. In the second condition, we manually translate the tactics learned by the evolutionary algorithm, and use them for dynamic scripting. In the third condition, this translation is automated. We found that dynamic scripting performs best under the third condition, and both of the latter conditions outperform manual tactic encoding. We discuss the implications of these results, and the performance of dynamic scripting for adaptive game AI from the perspective of machine learning research and commercial game development.