Battlespace: using AI to understand friendly vs. hostile decision dynamics in MDO

Abstract

Future Multi Domain Operation (MDO) wargaming will rely on Artificial Intelligence/Machine Learning (AI/ML) algorithms to aid and accelerate complex Command and Control decision-making. This requires an interdisciplinary effort to develop new algorithms that can operate in dynamic environments with changing rules, uncertainty, individual biases, changing cognitive states, as well as the capability to rapidly mitigate unexpected hostile capabilities and exploit friendly technological capabilities. Building on recent advancements in AI/ML algorithms, we believe that new algorithms for learning, reasoning under uncertainty, game theory with three or more players, and interpretable AI can be developed to aid in complex MDO decision-making. To achieve these goals, we developed a new flexible MDO warfighter machine interface game, Battlespace, to investigate and understand how human decision-making principles can be leveraged by and synergized with AI. We conducted several experiments with human vs. random players operating in a fixed environment with fixed rules, where the overall goal of the human players was to collaborate to either capture the opponents’ flags or eliminate all of their units. Then, we analyzed the evolution of the games and identified key features that characterized the human players’ strategies and their overall goal. We then followed a Bayesian approach to model the human strategies and developed heuristic strategies for a simple AI agent. Preliminary analysis revealed that following the human agents’ strategy in the capture the flag games produced the greatest winning percentage and may be useful for gauging the value of intermediate game states for developing the coordinated action planning of reinforcement learning algorithms.