Winning Prediction in WoW Strategy Game Using Evolutionary Learning

Abstract

Over the past decades, real-time strategy (RTS) games have steadily gained in popularity and have become common in video game leagues. However, a big challenge for creating human-level game AI is the different traits of races of opponents and their locations of enemy units are partially observable. To overcome this limitation, we explore evolutionary-based approach for estimating the location of enemy units that have been encountered. In this paper, we propose an efficient framework to predict the winning ratio between the different races used in the real-time strategy game. We represent state estimation as an optimization problem, and automatically learn parameters for the evolutionary-based model by learning a corpus of expert Star Craft replays. The evolutionary-based model tracks opponent units and provides conditions for activating tactical behaviors in our Star Craft boot. Our results show that incorporating a learned evolutionary-based model improves the performance of EISBot by 60% over baseline approaches.