  This paper covers a number of approaches that leverage Artificial
Intelligence algorithms and techniques to aid Unmanned Combat Aerial Vehicle
(UCAV) autonomy. An analysis of current approaches to autonomous control is
provided followed by an exploration of how these techniques can be extended and
enriched with AI techniques including Artificial Neural Networks (ANN),
Ensembling and Reinforcement Learning (RL) to evolve control strategies for
UCAVs.
