Dimensions-based classifier for strategy classification of opponent models in real-time strategy games

Abstract

Real-time strategy games are strategic war games where two or more players operate on a virtual battlefield, controlling resources, buildings, units and technologies to achieve victory by destroying others. Achieving victory depends on selecting a suitable plan (set of actions), selecting a suitable plan depends on building an imagination (building a model) of the opponent to know how to deal with. This imagination is the opponent model, the stronger the opponent modelling process is, the more accurate the selected suitable plan is and consequently the higher probability achieving the victory is. One of the environment's challenges in real-time strategy games is that classifying the opponent model is game specific. This paper introduces a new methodology through which we can classify the observed opponent model in a way that is not game specific. Our methodology includes two paths, only one of them is executed per real-time strategy game type (per opponent models trained), which means that different type of real-time strategy games will execute different paths of the two paths of our methodology.