Autonomous generation of different courses of action in mechanized combat operations

TL;DR

This paper introduces a methodology for generating and evaluating multiple courses of action in mechanized combat, aiding decision-making during military operations by considering opponent status and evolving conditions.

Contribution

It presents a systematic approach to produce and assess thousands of action alternatives in real-time, integrating opponent analysis and dynamic updates for improved military decision support.

Findings

Generated thousands of action alternatives rapidly

Evaluated outcomes considering opponent and terrain factors

Supported dynamic decision-making in evolving combat scenarios

Abstract

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.