Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games
This addresses the challenge of strategic decision-making under uncertainty for players in RTS games, representing an incremental improvement in modeling techniques.
The paper tackles the problem of inferring opponent strategies in real-time strategy games like Starcraft from limited scouting observations, presenting a dynamic Bayes net model that combines generative modeling with evidence incorporation to infer unobserved game aspects.
In typical real-time strategy (RTS) games, enemy units are visible only when they are within sight range of a friendly unit. Knowledge of an opponent's disposition is limited to what can be observed through scouting. Information is costly, since units dedicated to scouting are unavailable for other purposes, and the enemy will resist scouting attempts. It is important to infer as much as possible about the opponent's current and future strategy from the available observations. We present a dynamic Bayes net model of strategies in the RTS game Starcraft that combines a generative model of how strategies relate to observable quantities with a principled framework for incorporating evidence gained via scouting. We demonstrate the model's ability to infer unobserved aspects of the game from realistic observations.