Impostors Among Us: An Agentic Approach to Identifying and Resolving Conflicts in Collaborative Network Environments

Abstract

Today’s networked cyber-physical environments contain a wide range of agents, including fixed sensors, unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs), which help accomplish the objective(s) of a given mission. These collaborating agents support informed decision making for humans to accomplish mission objectives such as surveillance or search and rescue. However, these agents and their sensors are subject to various failures, including power, communication, hardware, and environmental factors. In contested environments, these failures also result from the kinetic or cyber actions of the adversary. This can result in conflicting information and the loss of a shared notion of the truth, leading to impaired situational awareness and poor decision-making. To overcome this challenge, we present CONFLICTRESOLVER, a policy-driven knowledge graph framework that can identify agents that share conflicting information and resolve conflicts using agent negotiation and reasoning. In doing so, it also infers/updates the trustworthiness measures of the sensing agents. This framework analyzes the information available within the collaborative agents to identify conflicts, acquire mission-critical objectives from the operator, determine triggering actions, and self-organize and reconfigure the agents’ capabilities in accordance with mission-criticality to remain resilient in contested environments. As part of our test bed, we deploy UGVs equipped with multimodal sensors to demonstrate how agents handle conflicts and establish trust.