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A Study of Drone-based AI for Enhanced Human-AI Trust and Informed Decision Making in Human-AI Interactive Virtual Environments

Search and Rescue Operations (SRO) are notoriously difficult as they typically involve human operations in high risk and low visibility environments. Often, stakeholders only have a general perception of the possible adversities in the situational environment. Ultimately, the success of these operations is a function of the manpower available, the terrain of the region, informed-decision-making based on terrain mapping and objective success in completion of search and rescue tasks with lower human casualties. A practical solution to this problem is to leverage the use of autonomous systems such as drones and rescue robots that can scout the terrain to gather information to augment the rescue team’s capabilities and mission success rates. In situations, such as a combat search and rescue mission, the mission might call for a cooperative effort between a Human and AI Agent, whereby both are able to share intelligence and coordinate in decision-making tasks. In this work, we present several novel contributions through a combat search and rescue simulation scenario that leverages a drone-based AI autonomous system for detection of targets-of-interest in the environment as a basis for human-AI teaming study. In this research, we examine various human factor metrics for different modes of interactions between the human agent and AI-driven drone/autonomous system agent to include implications on human mission completion with and without drone-based AI target detection-derived human situational awareness and time to mission completion. In addition, we introduce innovative AI techniques to model human agent (player) - AI agent (drone) exchanges through a hostage rescue scenario-based simulation and explore incentive strategies directed towards the human agent to encourage adoption of AI-based autonomous system as a cooperative intelligence asset and improve human-AI teaming performance. The unification of both AI techniques to model Human-AI interaction and incentive mechanisms to encourage usage of autonomous systems sets the foundation for assessing the efficacy of AI in Human Agent Teams.