Coevolving Robust Build-Order Iterative Lists for Real-Time Strategy Games
We investigate and develop a coevolutionary approach to finding strong, robust build orders for real-Time strategy games. Which units to produce and the order in which to produce them is one important aspect of real-Time strategy gameplay. In real-Time strategy games, creating plans to address unit production problems are called 'build orders.' Our research compares build orders produced from a coevolutionary algorithm, genetic algorithm (GA), and hill climber (HC) to exhaustive search. GAs find the strongest build orders, while coevolution produces more robust build orders than a genetic algorithm or HC. Case injection into the coevolutionary teachset and population can be used to bias coevolution into producing build orders that beat specific opponents or play like specific players, while maintaining robustness. Finally, in this paper, we extend our representation by adding branching and iteration to the build-Action sequence and show that this more complex representation enables coevolution to find stronger build orders. We believe this study is a start toward a promising approach for creating strong, robust build orders for RTS games.
Computer games, genetic algorithms, real-Time strategy (RTS)
Ballinger, Christopher, Sushil Louis, and Siming Liu. "Coevolving robust build-order iterative lists for real-time strategy games." IEEE Transactions on Computational Intelligence and AI in Games 8, no. 4 (2016): 363-376.
IEEE Transactions on Computational Intelligence and AI in Games