μCCG, a CCG-based Game-Playing Agent for μRTS

摘要

This paper presents a Combinatory Categorial Grammar-based game playing agent called μCCG for the Real-Time Strategy testbed μRTS. The key problem that μCCG tries to address is that of adversarial planning in the very large search space of RTS games. In order to address this problem, we present a new hierarchical adversarial planning algorithm based on Combinatory Categorial Grammars (CCGs). The grammar used by our planner is automatically learned from sequences of actions taken from game replay data. We provide an empirical analysis of our agent against agents from the CIG 2017 μRTS competition using competition rules. μCCG represents the first complete agent to use a learned formal grammar representation of plans to adversarially plan in RTS games.