Churchill and Buro (2013) launched a line of research through Portfolio Greedy Search (PGS), an algorithm for adversarial real-time planning that uses scripts to simplify the problem's action space. In this paper we present a problem in PGS's search scheme that has hitherto been overlooked. Namely, even under the strong assumption that PGS is able to evaluate all actions available to the player, PGS might fail to return the best action. We then describe an idealized algorithm that is guaranteed to return the best action and present an approximation of such algorithm, which we call Nested-Greedy Search (NGS). Empirical results on MicroRTS show that NGS is able to outperform PGS as well as state-of-the-art methods in matches played in small to medium-sized maps.

The workshops took place October 8-9, 2012, at Stanford University. This report contains summaries of the activities of those four workshops. With the advent of the BWAPI StarCraft programming interface, interest in real-time strategy (RTS) game AI has increased considerably. At the 2011 AIIDE conference, several papers on the subject were presented, ranging from build order planning, over state estimation, to plan recognition. In addition, a panel discussion on RTS game AI took place, the StarCraft competition was discussed, prizes were awarded, and two exhibition match replays were shown.