ABSTRACT
Smart decision-making at the tactical level is important for AI agents to perform well in real-time strategy games, in which winning battles is crucial. Although human players can decide when and how to attack based on their experience, it is challenging for AI agents to estimate combat outcomes accurately. Prediction by running simulations is a popular method, but it uses significant computational resources and needs explicit opponent modeling in order to adjust to different opponents. Scripted behavior is a common choice for making such decisions, due to the ease of implementation and very fast execution. One choice that bypasses the need for extensive game knowledge and coding effort is to simulate the battle multiple times, without actually attacking in the game, and to record the outcomes. The original Lanchester equations represent simplified combat models: each side has identical soldiers and a fixed strength, which governs the proportion of enemy soldiers killed.
