Machine-learning researchers make many decisions when designing new models. They decide how many layers to include in neural networks and what weights to give inputs at each node. The result of all this human decision-making is that complex models end up being "designed by intuition" rather than systematically, says Frank Hutter, head of the machine-learning lab at the University of Freiburg in Germany. A growing field called automated machine learning, or autoML, aims to eliminate the guesswork. The idea is to have algorithms take over the decisions that researchers currently have to make when designing models.

Although automated machine learning has been around for almost a decade, researchers are still working to refine it. Last week, a new conference in Baltimore--which organizers described as the first international conference on the subject--showcased efforts to improve autoML's accuracy and streamline its performance. There's been a swell of interest in autoML's potential to simplify machine learning. Companies like Amazon and Google already offer low-code machine-learning tools that take advantage of autoML techniques. If these techniques become more efficient, it could accelerate research and allow more people to use machine learning. The idea is to get to a point where people can choose a question they want to ask, point an autoML tool at it, and receive the result they are looking for.

In the standard rules of the board game Risk, players take turns selecting or "drafting" the 42 territories on the board until all territories are owned. We present a technique for drafting territories in Risk that combines the Monte Carlo tree search algorithm UCT with an automated evaluation function. Created through supervised machine learning, this function scores outcomes of drafts in order to shorten the length of a UCT simulation. Using this approach, we augment an existing bot for the computer game Lux Delux, a clone of Risk. Our drafting technique is shown to greatly improve performance against the strongest opponents supplied with Lux Delux. The evidence provided indicates that territory drafting is important to overall success in Risk.