Learning Safe Interactions and Full-Control

This chapter is concerned with the problem of learning how to interact safely with complex automated systems. With large systems, human-machine interaction errors like automation surprises are more likely to happen. Full-control mental models are formal system abstractions embedding the required information to completely control a system and avoid interaction surprises. They represent the internal system understanding that should be achieved by perfect operators. However, this concept provides no information about how operators should reach that level of competence. This work investigates the problem of splitting the teaching of full-control mental models into smaller independent learning units. These units each allow to control a subset of the system and can be learned incrementally to control more and more features of the system. This chapter explains how to formalize the learning process based on an operator that merges mental models. On that basis, we show how to generate a set of learning units with the required properties