Contents I. Introduction
Human path planning decisions are ubiquitous: whenever we move, be it within rooms, buildings or cities, a path along several waypoints is needed. While robots can solve such tasks and strive for optimality within well-defined constraints, human guidance is often needed in practical applications in order to optimize or even just satisfy a range of ill-defined, task-specific criteria. Understanding this process can guide the design of user interfaces for robot control, to allow human operators to more effectively balance tasks and work more efficiently. In this study, we design a cognitively motivated model to explain and predict visually guided path planning decisions by human operators in a multi-robot control experiment. We propose metrics to evaluate model fit and show that it illustrates differential behavior across subjects, whose strategies do not always scale with the workload.
