AI Advancements and Mission Command: A Closer Reality Than Perceived
The U.S. Army is making significant strides in the development of artificial intelligence (AI) to enhance military command systems. The focus is on improving situational awareness and avoiding detection through Electromagnetic Spectrum (EMS) means.
One of the key advancements comes from the Army Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance (C5ISR) Center, which has developed a genetic algorithm-based capability. This innovation optimizes the timing of activities within the synchronization matrix, aiming to streamline operations and improve decision-making processes.
Virtual and augmented reality tools are another area of development. These tools offer more immersive training and mission rehearsal experiences, enhancing situational awareness, and allowing for dispersed collaboration. They are expected to play a crucial role in mission command, particularly in the context of Multi-Domain Operations (MDO), where the Army emphasizes the importance of rapidly exploiting windows of superiority.
The use of AI in mission command is gaining attention within the U.S. defense community. New programs are being initiated to secure or reinforce advantages over countries like China, Russia, and Turkey. However, challenges remain. The Army acknowledges that while it is making progress with developing data as a resource, many of the inputs needed to aid commanders in decision-making are not available, electronically or otherwise.
The Army's approach to AI decision aids is not just about technology. It's also about training and doctrine. If the current methods of following the military decision-making process are manual and effective, that's the process soldiers will use in battle. This perspective is rooted in over thirty years of history of attempting to develop intelligent support tools for mission command, with limited success.
Modular Open Systems Approach is gaining new life due to advancements in microservices architectures. This approach allows for self-contained microservices to be shared and combined to build new capabilities, potentially overcoming some of the past challenges.
There are now tools available that allow soldiers to combine data sets and produce novel analytics without advanced programming skills. This democratization of AI could significantly improve the Army's ability to analyze large data sets and simulate scenarios in real time, a key requirement for effective decision-making in modern warfare.
Current challenges include integrating advanced AI to accelerate decision-making, ensuring effective real-time recommendations for military resource deployment, and managing the ethical, regulatory, and operational complexities of deploying autonomous systems such as drones across air, land, and sea domains. Maintaining technological superiority in electronic warfare, cyberspace, and space operations while coordinating multi-sector AI projects amid political and bureaucratic constraints is another hurdle.
AI has become a dual-use technology, used by both military and commercial sectors. This could lead to increased research and development funds and trust in AI on the battlefield. However, it also raises concerns about the potential for AI to be used in unethical or destabilizing ways.
The use of loitering drones, hypersonic weapons, and AI in warfare is compressing the decision-making space, making it necessary to analyze, consider alternatives, and plan faster. Industry engagement is key to seeking near-term technologies, and the adoption of such capabilities will be successful only if supported by corresponding experimentation, leading to changes in training and in tactics, techniques, and procedures.
Recent advances in reinforcement learning techniques can automatically learn how to fight, potentially eliminating a bulk of development and maintenance costs. DARPA is pursuing the use of machine learning in multiple programs, including Adapting Cross-domain Kill-webs, Constructive Machine-learning Battles with Adversary Tactics, System-of-Systems Enhanced Small Unit, and Strategic Chaos Engine for Planning, Tactics, Experimentation, and Resiliency.
In 2020, a dispute over the Nagorno-Karabakh region led Azerbaijan to use Turkish-made loitering drones (Bayraktar TB2) to strike at Armenian tanks and command posts without warning, demonstrating the potential impact of AI and drone technology in modern conflict.
However, a digital representation of doctrine to be used as a rule set for AI decision aids is still lacking. This may prevent AI decision aids from overly constraining their outputs but may also raise scenarios that are simply unworkable from a tactical perspective.
In conclusion, the U.S. Army is making significant strides in the development of AI for military command systems. While challenges remain, advancements in technology, training, and doctrine offer promising possibilities for the future of AI in warfare.
