<p>Real-time strategy (RTS) games constitute one of the largest game genres today and have done so for the past decade. A central feature of real-time strategy games is opponent AI which is suggestively the “last frontier” of game development because the focus of research has primarily been on other components, graphics in particular. This has led to AI research being largely ignored within the commercial game industry but several methods have recently been suggested for improving the strategic ability of AI agents in real-time strategy games.</p><p>The aim of this project is to evaluate how a method called qualitative spatial reasoning can improve AI on a tactical level in a selected RTS game. An implementation of an AI agent that uses qualitative spatial reasoning has been obtained and an evaluation of its performance in an RTS game example monitored and analysed.</p><p>The study has shown that qualitative spatial reasoning affects AI agent’s behaviour significantly and indicates that it can be used to deduce a rule-base that increases the unpredictability and performance of the agent.</p>

Real-time strategy (RTS) games constitute one of the largest game genres today and have done so for the past decade. A central feature of real-time strategy games is opponent AI which is suggestively the “last frontier” of game development because the focus of research has primarily been on other components, graphics in particular. This has led to AI research being largely ignored within the commercial game industry but several methods have recently been suggested for improving the strategic ability of AI agents in real-time strategy games. The aim of this project is to evaluate how a method called qualitative spatial reasoning can improve AI on a tactical level in a selected RTS game. An implementation of an AI agent that uses qualitative spatial reasoning has been obtained and an evaluation of its performance in an RTS game example monitored and analysed. The study has shown that qualitative spatial reasoning affects AI agent’s behaviour significantly and indicates that it can be used to deduce a rule-base that increases the unpredictability and performance of the agent.