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Autonomous systems are making their way to the market. The transition from tasks performed by humans to tasks performed by machines begs for an answer to one of the most challenging questions in this area of research: Will humans understand and trust what a machine does? Analyzing human and machine behaviors offers the foundational steps toward finding answers to this question. This paper contributes a novel methodology for transforming low-level actions by each agent into high-level categorization of strategies to contrast the behaviors of humans and machines using a computational red teaming environment with a red (evader) and a blue (pursuer) agent. Two orthogonal sources of uncertainty were examined: the uncertainty in the blue agent's situation awareness about the red, and the red agent's uncertainty resulting from deceptive actions by the blue. For each uncertainty source, two different experiments were conducted by varying the controller of the red agent. In one experiment, the red agent was controlled by one of the 34 human subjects; and in the second, by an evolved neural network. The blue agent was controlled by a scripted rule-based system. In this time-critical task, the results revealed that humans tend to follow systemic and consistent strategies, sometimes ignoring the information available to them. On the other hand, machines tend to evolve more complex and diverse strategies. This finding calls for new computational intelligence techniques to enable the fusion of these different strategies into forms that each party can understand and use effectively.

Contrasting Human and Computational Intelligence Based Autonomous Behaviors in a Blue–Red Simulation Environment