Robustness of Middleware Communication in Contested and Dynamic Environments
Contested and dynamic environments with poor and unreliable network conditions are often encountered by military operations or crisis management systems. While it is a common occurrence for networks to behave poorly, or for system nodes to malfunction, most existing decision making algorithms have only been tested in perfect conditions. In this paper, we present an analysis of the robustness of reinforcement learning and evolutionary approaches employed in a communication middleware that operates in a contested environment. The SMARTNet middleware prioritizes and controls the messages sent by each node, with the aim of preserving network bandwidth. We evaluate the robustness of a reinforcement learning and an evolutionary computation implementations as SMARTNet executes in changing conditions, with nodes dropping out, and the network becoming congested both generally and for specific message types.