Aptima and the Cognitive Engineering Research Institute are developing a mixed-initiative decisionsupport system for planning multi-Unmanned Aerial System (UAS) missions. The prototype capability is called MIMIC: Mixed Initiative Machine for Instructed Computing. At the core of the system is a model that employs machine learning algorithms to learn from operators during mission planning, and to use what is learned to aid subsequent mission planning tasks. This paper reports on the design of the prototype algorithms, the early interface design, and a series of three experiments performed to support the design of the system. First, machine learning algorithms were implemented that use Markov Decision Processes (MDPs) and Bayesian inference to learn a model of the human operator's strategies. Second, a mission planning graphical user interface was designed to enable an operator to conduct basic multi-UAS mission planning, and to allow MIMIC to easily capture operator actions. Finally, three experiments were conducted (1) to identify typical operator planning priorities, in order to define the model's features and to gather planning data to train the algorithms, (2) to evaluate the model by comparing its outputs to operators' selfassessments of priorities and goals, and (3) to test the model's ability to predict what an operator's next actions will be, and compare the predictions to the actual operator actions. The MIMIC project represents a step toward increasing levels of UAS autonomy allowing for multi-UAS control by single operators.