Strategy games constitute a significant challenge for game AI, as they involve a large number of states, agents and actions. This makes indeed the decision and learning algorithms difficult to design and implement. Many commercial strategy games use scripts in order to simulate intelligence, combined with knowledge which is in principle not accessible to human players, such as the position of the enemy base or the offensive power of its army. Nevertheless, recent research on adaptive techniques has shown promising results. The goal of this paper is to present the extension such a research methodology, named Strada, so that it is made applicable to the real-time strategy platform ORTS. The adaptations necessary to make Strada applicable to ORTS are detailed and involve the use of dynamic tactical points and specific training scenario for the learning AI. Two sets of experiments are conducted to evaluate the performances of the new method.