Autonomous generation of different courses of action in mechanized combat operations
This addresses the need for automated decision support in military operations, but it appears incremental as it builds on existing field manuals and sequential frameworks.
The paper tackles the problem of supporting decision-making in military ground combat by proposing a methodology that generates and evaluates thousands of alternative courses of action for a mechanized battalion, resulting in recommendations with superior outcomes based on opponent status and actions.
In this paper, we propose a methodology designed to support decision-making during the execution phase of military ground combat operations, with a focus on one's actions. This methodology generates and evaluates recommendations for various courses of action for a mechanized battalion, commencing with an initial set assessed by their anticipated outcomes. It systematically produces thousands of individual action alternatives, followed by evaluations aimed at identifying alternative courses of action with superior outcomes. These alternatives are appraised in light of the opponent's status and actions, considering unit composition, force ratios, types of offense and defense, and anticipated advance rates. Field manuals evaluate battle outcomes and advancement rates. The processes of generation and evaluation work concurrently, yielding a variety of alternative courses of action. This approach facilitates the management of new course generation based on previously evaluated actions. As the combat unfolds and conditions evolve, revised courses of action are formulated for the decision-maker within a sequential decision-making framework.