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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.10207 |
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| _version_ | 1866911263644188672 |
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| author | Autenrieb, Johannes Ostermann, Ole |
| author_facet | Autenrieb, Johannes Ostermann, Ole |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalized Intelligence for Tactical Decision-Making: Large Language Model-Driven Dynamic Weapon Target Assignment Autenrieb, Johannes Ostermann, Ole Systems and Control 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. |
| title | Generalized Intelligence for Tactical Decision-Making: Large Language Model-Driven Dynamic Weapon Target Assignment |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.10207 |