Adversarial Planning Through Strategy Simulation

Abstract

Adversarial planning in highly complex decision domains, such as modern video games, has not yet received much attention from AI researchers. In this paper, we present a planning framework that uses strategy simulation in conjunction with Nash-equilibrium strategy approximation. We apply this framework to an army deployment problem in a real-time strategy game setting and present experimental results that indicate a performance gain over the scripted strategies that the system is built on. This technique provides an automated way of increasing the decision quality of scripted AI systems and is therefore ideally suited for video games and combat simulators