Adaptive Automation Based on an Object-Oriented Task Model: Implementation and Evaluation in a Realistic C2 Environment

Abstract

Staffing reduction initiatives and more complicated military operations lead to a higher cognitive workload in command and control (C2) environments. Extending automation with adaptive capabilities can aid the human in overcoming cognitive workload challenges. At present, most adaptive automation research has focused on laboratory experiments and only limited research has aimed to implement and validate adaptive automation in a real-world setting. The objective of the present study was to investigate the effects of adaptive automation in precisely such a setting, extending the scientific knowledge base of adaptive systems with an evaluation of a real-world adaptive task. Implementing adaptive automation in a real-world C2 setting required extending current adaptive automation theories with an object-oriented task model and a hybrid triggering mechanism. The extended model was evaluated with eight naval officers using a high-fidelity C2 environment and showed an overall efficiency effect of 60%. Furthermore, no negative side effects of adaptive automation have been found, and the data show that the scenarios were manipulated correctly. In addition, the positive efficiency effects appear most strongly in the more complicated asymmetrical scenarios (65%). This latter conclusion shows that adaptive automation can be a valuable contribution to future C2 systems.