Decision superiority is fundamental to success in military endeavours. Artificial intelligence is increasingly relevant to achieving this decision superiority. Differentiable decision trees (DDTs), also known as propositional logic networks (ProLoNets), allow human subject matter expertise to be represented as decision trees that can be encoded as artificial neural networks, and which are amenable to deep reinforcement learning (RL) algorithms. Consequently, DDTs allow us to warm-start RL and have a high degree of explainability. In this presentation, the fundamental ideas behind DDTs will be introduced and demonstrated in the context of a Kaggle Competition based on Halite: a video game consisting of 4 competing players where the objective is to mine the most halite. Players do so by building and controlling spaceships. Whilst this toy problem is a far-cry from Defence applications, the lessons learned about building and deploying DDTs are insightful.

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