Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Co-evolving real-time strategy game players
Co-evolving real-time strategy game players
A model of visual organization for the game of GO
AFIPS '69 (Spring) Proceedings of the May 14-16, 1969, spring joint computer conference
Evolving coordinated spatial tactics for autonomous entities using influence maps
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Combining influence maps and cellular automata for reactive game agents
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Playing to learn: case-injected genetic algorithms for learning to play computer games
IEEE Transactions on Evolutionary Computation
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Real Time Strategy (RTS) games provide a representation of spatial tactics with group behaviour. Often tactics will involve using groups of entities to attack from different directions and at different times of the game, using coordinated techniques. Our goal in this research is to learn tactics which are challenging for human players. The method we apply to learn these tactics, is a coevolutionary system designed to generate effective team behavior. To do this, we present a unique Influence Map representation, with a coevolutionary technique that evolves the maps together for a group of entities. This allows the creation of autonomous entities that can move in a coordinated manner. We apply this technique to a naval RTS island scenario, and present the successful creation of strategies demonstrating complex tactics.