OPPONENT MODELS PREPROCESSING IN REAL-TIME STRATEGY GAMES

Creating a human-like computer player in real-time strategy games requires huge number of opponent models, these models must be preprocessed to either focus on accuracy or performance according to our needs. In order to preprocess these models accurately, we need to detect their type. Opponent models' type can be complex or simple. Complex opponent models are low variance models whose differences in features' values are low, so in order to accurately separate between these models, we need to preprocess them by increasing their dimensions. Simple opponent models are high variance models whose differences in features' values are high, so in order to separate between these models in a reasonable time, we need to preprocess them to decrease their dimensions, if possible, without accuracy or data loss.