Retrieve Instruction from Manual for Strategy Games

In this project, we study the task of helping an automated player win a computer game by reading a strategic user’s guide designed for human players. In complex computer games such as Star Craft, War Craft, and Civilization, finding a winning strategy is challenging even for humans. Therefore, human players typically rely on manuals and guides. Recently, researchers have tried to using such textual information to train an automated player(Branavan, Silver, and Barzilay 2011). Our goal, is to better understand the retrieval model used in their paper in terms of algorithms and metrics from the field of information retrieval. Our results provide some evidence that inverse document frequency out performs recurrent neural networks at assisting human players, and could be used as a baseline for evaluating retrieval models used in playing games like this.