The War of the Future

An extract from the chapter AI, Autonomy, and the Third Offset Strategy

Robert O Work

Algorithmic Warfare And Human-Machine Collaborative Battle Networks

Universal digitization and widespread insertion of autonomous systems at rest and in motion into battle network grids are expected to lead to new forms of human-machine collaboration and human-machine and machine-machine combat teaming. These, in turn, could lead to new operational concepts and organizational constructs. The framers of the 3OS expected their emergence would signal the development of a new type of “algorithmic warfare”—the next logical evolutionary step in battle network development. However, they also posited that the “evolutionary” emergence of algorithmic warfare might lead to “revolutionary” battlefield results if it allowed joint force guided munitions battle networks to operate at a higher operational tempo than any adversary network.

Higher operational tempo would come from the deliberate and widespread insertion of human-supervised narrow-task autonomous systems into all four grids of joint multidomain guided munitions battle networks.

For example, sensors that are equipped with field programmable gate arrays and are capable of onboard machine learning could discern objects on their own without having to download the raw data to a processing center. Analytical tools powered by machine learning could assist intelligence analysts to discover patterns in complex multidomain scenarios characterized by enormous amounts of heterogeneous data. Decision aid tools could help develop courses of action for a commander’s review and consideration. Autonomous cognitive cyber and electronic warfare tools could help win the battle for electromagnetic spectrum superiority. Network-enabled autonomous weapons could coordinate their attacks with other weapons or manned platforms or provide information on enemy defenses to trailing salvos, which could then modify their attack plans (i.e., course of action).

At some point, the 3OS proponents hypothesized that this aggressive pursuit of narrow-task AI-enabled autonomous systems in all battle network grids would lead to the emergence of a new type of human-machine collaborative battle network. These new networks would be the physical manifestation of algorithmic warfare, capable of

• more rapid sense-making of high heterogeneity and volume of data
• more rapid understanding of the operational environment
• more rapid development of a common joint multidomain operational picture, shared more quickly throughout the force
• more rapid development of relevant courses of action and plans
• more rapid force-wide understanding and appreciation of commander’s intent
• more rapid and relevant decisions, promulgated faster to manned, unmanned, and human-machine combat teams and combat organizations

In addition to enabling a much higher and more sustained op