Generation of rule-based adaptive strategies for a collaborative virtual simulation environment

Real Time Strategy Games (RTSG) are a strong test bed for AI research, particularly on the subject of unsupervised learning. They offer a challenging, dynamic environment with complex problems that often have no perfect solutions. Learning classifier systems are rule-based machine learning techniques that rely on a genetic algorithm to discover a knowledge map used to classify an input space into a set of actions. This paper focuses on the use of accuracy-based learning classifier system (XCS) as the learning mechanism for generating adaptive strategies in a real time strategy game. The performance and adaptability of the developed strategies with the XCS is analyzed by facing these against scripted opponents on an open source game called Wargus. Results show that the XCS module is able to learn adaptive strategies effectively and achieve the objectives of each training scenario.