Strategy Generation for Multiunit Real-Time Games via Voting

Real-time strategy (RTS) games are a challenging application for artificial intelligence (AI) methods. This is because they involve simultaneous play and adversarial reasoning that is conducted in real time in large state spaces. Many AI methods for playing RTS games rely on hard-coded strategies designed by human experts. The drawback of using such strategies is that they are often unable to adapt to new scenarios during gameplay. The contribution of this paper is a new approach, called strategy creation via voting (SCV), that uses a voting method to generate a large set of novel strategies from existing expert-based ones. Then, SCV uses an opponent modeling scheme during the game to choose which strategy from the generated pool of possibilities to use. By repeatedly choosing which strategy to use, SCV is able to adapt to different scenarios that might arise during the game. We implemented SCV as a bot for $\mu$ RTS, a recognized RTS testbed. The results of a detailed empirical study show that SCV outperforms all approaches tested in matches played on large maps and is competitive in matches played on smaller maps.