This article presents the results of the first edition of the microRTS (μRTS) 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, focusing on address-ing problems such as balancing long-term and short-term search, the use of machine learning to learn how to play against certain opponents, and finally, dealing with partial observability in RTS games.