전영역 인식 기반 지휘관 AI 의사결정 지원모델 QFSVDM: JADC2 환경의 비가환 정보 갱신, 인간 중심 검증체계 및 합성자료 반복 실험

오경원^*

한남대학교 기계공학과 교수

An AI Decision-Support Model for Commanders Based on All-Domain Awareness (QFSVDM): Non-Commutative Information Updates, Human-Centered Validation, and Repeated Synthetic-Data Experiments in a JADC2 Environment

Kyungwon Oh^*

Professor, Department of Mechanical Engineering, Hannam University

Correspondence to: *Kyungwon Oh E-mail: kwoh@hnu.kr

초록

본 연구는 전영역 인식과 JADC2 환경에서 지휘관에게 미래 임무 여건의 확률과 근거를 제공하는 AI 의사결정 지원모델 QFSVDM을 제안한다. 밀도행렬과 비가환 갱신으로 순차 정보를 표현하되 물리적 양자계를 가정하지 않으며, 대각 충분조건에서 부호 자유 고전 필터로 정확히 환원된다. 원천·수신·이용 가능시각을 구분하고 지휘관의 수용·수정·보류를 기록한다. 세 합성자료 생성과정의 독립 시드 153개에서 비교한 결과, 비대각 확장은 해당 구조가 있을 때만 고전 필터보다 낮은 표본외 로그손실을 보였으며, 시차 입력 로짓도 이득의 일부를 재현했다. 결과는 합성자료 검증에 한정되며 전장자료 학습이나 작전효과를 입증하지 않는다. 자료 품질·확률 예측·인간–AI 협업·통신 저하 복원성의 단계별 검증체계를 제시한다.

Abstract

This study proposes the Quantum-like Future-State Vector Decision Model (QFSVDM), an AI decision-support model providing commanders with future mission-condition probabilities and evidence based on all-domain awareness in JADC2 environments. Density matrices and non-commutative updates represent sequential information without assuming a physical quantum battlefield. The model extends a signed classical filter, reduces exactly to it under sufficient diagonal conditions, distinguishes source, receipt, and availability times, and records commanders’ acceptance, revision, or deferral. Across three synthetic generating processes and 153 independent seeds, the off-diagonal extension reduced out-of-sample log loss relative to the classical filter only when off-diagonal structure existed. A lagged-input logit reproduced part of this gain. These findings do not establish battlefield-data learning or operational effectiveness. A staged validation framework separates data quality, probabilistic forecasting, human–AI collaboration, and resilience under degraded communications.

Keywords:

All-Domain Awareness, JADC2, Joint All-Domain Command and Control, Command Decision Support, Human-AI Collaboration, QFSVDM, Quantum-like Future-State Vector Decision Model

키워드:

전영역 인식, 합동 전영역 지휘통제, 지휘관 의사결정 지원, 인간-AI 협업, 양자유사 미래상태벡터 의사결정 모델

Acknowledgments

이 논문은 2026학년도 한남대학교 학술연구비 지원에 의하여 연구되었음

References

U.S. Department of Defense, Summary of the Joint All-Domain Command and Control Strategy, 2022. 공식 원문.
NATO Allied Command Transformation, Multi-Domain Operations. 공개 설명, 2026-09-06 확인. 공식 원문.
M. R. Endsley, ‘Toward a Theory of Situation Awareness in Dynamic Systems,’ Human Factors, VOL. 37, NO. 1, 1995, pp. 32-64. [https://doi.org/10.1518/001872095779049543]
U.S. Department of Defense, Chief Digital and Artificial Intelligence Office, 2023 Data, Analytics, and Artificial Intelligence Adoption Strategy — Fact Sheet (2023-11-02), 2023. 공식 원문.
NATO, Summary of NATO’s Revised Artificial Intelligence (AI) Strategy, July 10, 2024. 공식 원문.
National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1, 2023.
John D. Lee & Katrina A. See, ‘Trust in Automation: Designing for Appropriate Reliance,’ Human Factors, VOL. 46, NO. 1, 2024, pp. 50-80. [https://doi.org/10.1518/hfes.46.1.50_30392]
Leslie Pack Kaelbling, Michael L. Littman, & Anthony R. Cassandra, ‘Planning and Acting in Partially Observable Stochastic Domains,’ Artificial Intelligence, VOL. 101, NO. 1-2, 1998, pp. 99-134. [https://doi.org/10.1016/S0004-3702(98)00023-X]
Jerome R. Busemeyer & Peter David Bruza, Quantum Models of Cognition and Decision (2nd ed.), Cambridge University Press, 2025. [https://doi.org/10.1017/9781009205351]
Zheng Wang, Tyler Solloway, Richard M. Shiffrin, & Jerome R. Busemeyer, ‘Context Effects Produced by Question Orders Reveal Quantum Nature of Human Judgments,’ Proceedings of the National Academy of Sciences, VOL. 111, NO. 26, 2014, pp. 9431-9436. [https://doi.org/10.1073/pnas.1407756111]
T. Baumgratz, M. Cramer, & M. B. Plenio, ‘Quantifying Coherence,’ Physical Review Letters, VOL. 113, 2014, Article 140401. [https://doi.org/10.1103/PhysRevLett.113.140401]
Olivier Ledoit & Michael Wolf, ‘A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices,’ Journal of Multivariate Analysis, VOL. 88, NO. 2, 2024, pp. 365-411. [https://doi.org/10.1016/S0047-259X(03)00096-4]
Masanao Ozawa, ‘Quantum Measuring Processes of Continuous Observables,’ Journal of Mathematical Physics, VOL. 25, NO. 1, 1984, pp. 79-87. [https://doi.org/10.1063/1.526000]
G. Lindblad, ‘On the Generators of Quantum Dynamical Semigroups,’ Communications in Mathematical Physics, VOL. 48, 1976, pp. 119-130. [https://doi.org/10.1007/BF01608499]
Vittorio Gorini, Andrzej Kossakowski, & E. C. G. Sudarshan, ‘Completely Positive Dynamical Semigroups of N-Level Systems,’ Journal of Mathematical Physics, VOL. 17, NO. 5, 1976, pp. 821-825. [https://doi.org/10.1063/1.522979]
R. B. Davies, ‘Hypothesis Testing When a Nuisance Parameter Is Present Only Under the Alternative,’ Biometrika, VOL. 64, NO. 2, 1977, pp. 247-254. [https://doi.org/10.1093/biomet/64.2.247]
A. Lebedev & A. Khrennikov, ‘Quantum-Like Modeling of the Order Effect in Decision Making: POVM Viewpoint on the Wang–Busemeyer QQ-Equality,’ In Infinite Dimensional Analysis, Quantum Probability and Related Topics (QP–PQ: Quantum Probability and White Noise Analysis). World Scientific. preprint v2: arXiv:1811.00045v2, . [https://doi.org/10.1142/9789811275999_0010]
Thomas Boyer-Kassem, Sébastien Duchêne, & Eric Guerci, ‘Testing Quantum-Like Models of Judgment for Question Order Effect,’ Mathematical Social Sciences, VOL. 80, 2016, pp. 33-46. [https://doi.org/10.1016/j.mathsocsci.2016.01.001]
Tilmann Gneiting & Adrian E. Raftery, ‘Strictly Proper Scoring Rules, Prediction, and Estimation,’ Journal of the American Statistical Association, VOL. 102, NO. 477, 2007, pp. 359-378. [https://doi.org/10.1198/016214506000001437]
Dimitris Politis & Joseph P. Romano, ‘The Stationary Bootstrap,’ Journal of the American Statistical Association, VOL. 89, NO. 428, 1994, pp. 1303-1313. [https://doi.org/10.1080/01621459.1994.10476870]