Description

In virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes, and for avoiding costly mistakes. Finding these strategies, however, usually requires substantial time, risk, and expensive trial and error. Recently, machine learning has been used to attack this problem, but unfortunately, most proposed solutions are “black box” algorithms, with underlying logic unclear to humans, significantly limiting their practical use and scientific value. In this work, we propose an alternative approach: repurposing machine learning to discover optimal, comprehensible strategies which can be understood, transferred, and used by humans directly. Through three common decision-making problems found in scheduling, we demonstrate the implementation and feasibility of this approach, as well as its great potential to attain optimal results.