Introduction
Artificial Intelligence (AI), like any science, must rely on reproducible experiments to validate results. However, reproducing results from AI research publications is not easily accomplished, and becoming ever more problematic as the real-world success work to keep pace with the hype and inflated expectations. This may be because AI research has its own unique reproducibility challenges. For example, these include (1) the use of analytical methods that are still a focus of active investigation and (2) problems due to non-determinism in standard benchmark environments and variance intrinsic to AI methods. Acknowledging these difficulties, empirical AI research should be adequately documented so that the experiments and results are clearly described.