Abstract

This article presents the results of the first edition of the microRTS AI competition, which was hosted by the IEEE Computational Intelligence in Games (CIG) 2017 conference. The goal of the competition is to spur research on AI techniques for real-time strategy (RTS) games. In this first edition, the competition received three submissions, each addressing problems such as balancing long-term and short-term search, using machine learning to learn how to play against certain opponents, and finally, dealing with partial observability in RTS games.