Experimental evaluation of cognitive agents for collaboration in human-autonomy cyber defense teams

Autonomous agents are becoming increasingly prevalent and capable of collaborating with humans on interdependent tasks as teammates. There is increasing recognition that human-like agents might be natural human collaborators. However, there has been limited work on designing agents according to the principles of human cognition or in empirically testing their teamwork effectiveness. In this study, we introduce the Team Defense Game (TDG), a novel experimental platform for investigating human-autonomy teaming in cyber defense scenarios. We design an agent that relies on episodic memory to determine its actions ( Cognitive agent ) and compare its effectiveness with two types of autonomous agents: one that relies on heuristic reasoning ( Heuristic agent ) and one that behaves randomly ( Random agent ). These agents are compared in a human-autonomy team (HAT) performing a cyber-protection task in the TDG. We systematically evaluate how autonomous teammates’ abilities and competence impact the team’s interaction and outcomes. The results revealed that teams with Cognitive agents are the most effective partners, followed by teams with Heuristic and Random agents. Evaluation of collaborative team process metrics suggests that the cognitive agent is more adaptive to individual play styles of human teammates, but it is also inconsistent and less predictable than the Heuristic agent. Competent agents (Cognitive and Heuristic agents) require less human effort but might cause over-reliance. A post-experiment questionnaire showed that competent agents are rated more trustworthy and cooperative than Random agents. We also found that human participants’ subjective ratings correlate with their team performance, and humans tend to take the credit or responsibility for the team. Our work advances HAT research by providing empirical evidence of how the design of different autonomous agents (cognitive, heuristic, and random) affect team performance and dynamics in cybersecurity contexts. We propose that autonomous agents for HATs should possess both competence and human-like cognition while also ensuring predictable behavior or clear explanations to maintain human trust. Additionally, they should proactively seek human input to enhance teamwork effectiveness.