Cooperation in Human-Agent Systems to Support Resilience

Abstract

Objective: This study uses a dyadic approach to understand human-agent cooperation and system resilience. Background: Increasingly capable technology fundamentally changes human-machine relationships. Rather than reliance on or compliance with more or less reliable automation, we investigate interaction strategies with more or less cooperative agents. Method: A joint-task microworld scenario was developed to explore the effects of agent cooperation on participant cooperation and system resilience. To assess the effects of agent cooperation on participant cooperation, 36 people coordinated with a more or less cooperative agent by requesting resources and responding to requests for resources in a dynamic task environment. Another 36 people were recruited to assess effects following a perturbation in their own hospital. Results: Experiment 1 shows people reciprocated the cooperative behaviors of the agents; a low-cooperation agent led to less effective interactions and less resource sharing, whereas a high-cooperation agent led to more effective interactions and greater resource sharing. Experiment 2 shows that an initial fast-tempo perturbation undermined proactive cooperation—people tended to not request resources. However, the initial fast tempo had little effect on reactive cooperation—people tended to accept resource requests according to cooperation level. Conclusion: This study complements the supervisory control perspective of human-automation interaction by considering interdependence and cooperation rather than the more common focus on reliability and reliance. Application: The cooperativeness of automated agents can influence the cooperativeness of human agents. Design and evaluation for resilience in teams involving increasingly autonomous agents should consider the cooperative behaviors of these agents.