Feature Selection for Learning from Demonstration in Minecraft

Learning from Demonstration has the potential to enable the crafting of behavior for non-player characters, allies, and enemies without requiring programming knowledge. This paper focuses on addressing two key problems of LfD when applied to games. The first is data sequentiality, when actions might be influenced by previous environmental states/actions, instead of just the current state. The second is having structured representations of data, where data is provided as an arbitrary number of predicates instead of a fixed-length vector. In this paper, we evaluate a collection of feature selection strategies to address these problems in the context case-based learning algorithms in the domain of Minecraft.