  Scientific discovery concerns finding patterns in data and creating
insightful hypotheses that explain these patterns. Traditionally, this process
required human ingenuity, but with the galloping advances in artificial
intelligence (AI) it becomes feasible to automate some parts of scientific
discovery. In this work we leverage AI for strategy discovery for understanding
human planning. In the state-of-the-art methods data about the process of human
planning is often used to group similar behaviors together and formulate verbal
descriptions of the strategies which might underlie those groups. Here, we
automate these two steps. Our method utilizes a new algorithm, called
Human-Interpret, that performs imitation learning to describe sequences of
planning operations in terms of a procedural formula and then translates that
formula to natural language. We test our method on a benchmark data set that
researchers have previously scrutinized manually. We find that the descriptions
of human planning strategies obtained automatically are about as understandable
as human-generated descriptions. They also cover a substantial proportion of of
relevant types of human planning strategies that had been discovered manually.
Our method saves scientists' time and effort as all the reasoning about human
planning is done automatically. This might make it feasible to more rapidly
scale up the search for yet undiscovered cognitive strategies to many new
decision environments, populations, tasks, and domains. Given these results, we
believe that the presented work may accelerate scientific discovery in
psychology, and due to its generality, extend to problems from other fields.
