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Measuring complacency in humans interacting with autonomous agents in a multi-agent system

With advances in machine learning, autonomous agents are increasingly able to navigate uncertain operational environments, as is the case within the multi-domain operations (MDO) paradigm. When teaming with humans, autonomous agents may flexibly switch between passive bystander and active executor depending on the task requirements and the actions being taken by partners (whether human or agent). In many tasks, it is possible that a well-trained agent's performance will exceed that of a human, in part because the agent's performance is less likely to degrade over time (e.g., due to fatigue). This potential difference in performance might lead to complacency, which is a state defined by over-trust in automated systems. This paper investigates the effects of complacency in human-agent teams, where agents and humans have the same capabilities in a simulated version of the predator-prey pursuit task. We compare subjective measures of the human's predisposition to complacency and trust using various scales, and we validate their beliefs by quantifying complacency through various metrics associated with the actions taken during the task with trained agents of varying reliability levels. By evaluating the effect of complacency on performance, we can attribute a degree of variation in human performance in this task to complacency. We can then account for an individual human's complacency measure to customize their agent teammates and human-in-the-loop requirements (either to minimize or compensate for the human's complacency) to optimize team performance.