Evolving neural networks through augmenting topologies
Evolutionary Computation
Human-Level AI's Killer Application: Interactive Computer Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
AI Techniques for Game Programming
AI Techniques for Game Programming
Automatically acquiring domain knowledge for adaptive game AI using evolutionary learning
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
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In this paper, we present an algorithm to imitate a game player's play patterns using a real-time neuroevolution (NE); the examples of the patterns can be moving and firing units. Our algorithm to learn and imitate is possible to be executed during gameplay. To test effectiveness of our algorithm, we made an application similar to the StarcraftTM. By using our method, a game player can avoids tediously repeating labors to control units. Moreover, applying this to enemy agents makes it possible to play more difficult and exciting games. From experimental results, we found that agents' ability to imitate a game player's unit control patterns could make human-like agents, and also we found that adaptive game AIs, especially the real-time NE, are efficient in such imitation problems.