Technology Category
Humans and AI: Planning and Decision Support for Human-Machine TeamsPlanning, Routing, and Scheduling: Temporal PlanningMultiagent Systems: Multiagent Planning
Autonomous generation of different courses of action in mechanized combat operations
🤖 AI Summary
To address the slow autonomous decision-making and poor adaptability of tactical plans in dynamic, adversarial mechanized ground combat environments, this paper proposes a real-time action plan generation and evolution method based on a sequential decision-making framework. The method integrates rule-based reasoning, battlefield modeling, and simulation techniques to concurrently generate and evaluate thousands of feasible maneuver paths under multidimensional constraints—including force posture, unit composition, offensive/defensive mission types, and advance velocity. A closed-loop feedback mechanism enables dynamic plan revision and performance optimization. Compared with conventional static tactical planning approaches, the proposed method significantly improves both plan generation speed and environmental adaptability. Experimental results demonstrate rapid generation of superior alternative strategies over baseline plans, achieving over 40% improvement in command decision-making efficiency. This work provides a scalable technical foundation for adaptive operations in complex, time-critical battlefield scenarios.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Generates alternative courses of action for mechanized battalions
Evaluates combat recommendations based on opponent status and actions
Supports dynamic decision-making as battlefield conditions evolve
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generates thousands of alternative combat action plans
Evaluates actions based on opponent status and battlefield conditions
Provides revised sequential recommendations as combat evolves
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