Boids Cycling Boyd's Loop: The Nexus of Artificial Intelligence and Joint Planning

The role of the commander and their intuition has remained constant while the character of war has evolved. In an operational environment increasing in velocity and complexity due to perpetual technological progress, the Joint Planning Process (JPP) suffers from path dependency, remaining an industrial era process only periodically revised to tackle increased complexity, failing to address the core analog processes while ignoring incorporation of digital possibilities. Re-envisioning JPP to incorporate advances in Artificial Intelligence (AI) and Machine Learning (ML) would invert the current method of reverse planning in theoretical hindsight from envisioned end state to a process grounded in the present reality, searching a range of courses of action through multiple decision trees, agilely and ceaselessly synchronizing to converge the multi-domain effects required to dominate a complex adaptive system. Through this forward exploration, insights of interacting with an adapting system emerge, and commanders are able to distill complexity and think in time, making often non-intuitive decisions in the near term in order to set up a decisive winning action in the future. Man-machine teaming within JPP will not only outpace the forecasting ability of the current process but will provide a capable receptor for insights from current DoD data analytics initiatives animating the environment and adversary as adaptive agent, gaining vital insight in time, across all domains, ultimately providing a better vantage point for manifesting coup d'oeil of the future.