Toward Human-in-the-Loop Supervisory Control for Cyber-Physical Networks

TL;DR

This paper introduces a human decision-making model based on neuroscience to improve supervisory control in cyber-physical networks, accounting for factors like stress and emergencies.

Contribution

It develops a novel decision model integrating neuroscience principles and applies it to optimize human-in-the-loop supervisory control systems.

Findings

Model explains strategy shifts from compensatory to heuristic under different conditions.

Incorporates stress and emergency effects into decision-making model.

Enhances supervisory control efficiency through adaptive human decision modeling.

Abstract

This work proposes a novel approach to include a model of making decision in human brain into the control loop. Employing the methodology developed in mathematical neuroscience, we construct a model that accounts for quality of human decision in supervisory tasks. We specifically focus on adaptive gain theory and the strategy selection problem. The proposed model is shown to be capable of explaining the change of a strategy from compensatory to heuristics in different conditions. We also propose a method to incorporate the effect of internal and external parameters such as stress level and emergencies in the decision model. The model is employed in a supervisory controller that dispatches the jobs between autonomy and a human supervisor in an efficient way.