Generalized Intelligence for Tactical Decision-Making: Large Language Model-Driven Dynamic Weapon Target Assignment

TL;DR

This paper presents a novel LLM-driven framework for dynamic weapon target assignment in aerospace defense, enabling adaptive, context-aware decision-making that surpasses traditional optimization methods.

Contribution

It introduces a large language model-based approach to tactical WTA, integrating generalized intelligence for real-time, adaptive decision-making in complex environments.

Findings

Enhanced adaptability and consistency in target assignments

Reduced switching of assignments in dynamic scenarios

Improved mission-level prioritization and reasoning

Abstract

Modern aerospace defense systems increasingly rely on autonomous decision-making to coordinate large numbers of interceptors against multiple incoming threats. Conventional weapon-target assignment (WTA) algorithms, including mixed-integer programming and auction-based methods, show limitations in dynamic and uncertain tactical environments where human-like reasoning and adaptive prioritization are required. This paper introduces a large language model (LLM) driven WTA framework that integrates generalized intelligence into cooperative missile guidance. The proposed system formulates the tactical decision process as a reasoning problem, in which an LLM evaluates spatial and temporal relationships among interceptors, targets, and defended assets to generate real-time assignments. In contrast to classical optimization methods, the approach leverages contextual mission data such as threat direction, asset priority, and closing velocity to adapt dynamically and reduce assignment switching. A dedicated simulation environment supports both static and dynamic assignment modes. Results demonstrate improved consistency, adaptability, and mission-level prioritization, establishing a foundation for integrating generalized artificial intelligence into tactical guidance systems.