Genetic Fuzzy based Artificial Intelligence for Unmanned Combat Aerial Vehicle Control in Simulated Air Combat Missions

Repeatable determinism using non-random weight initialisations in smart city applications of deep learning

Rudd-Orthner, R. N. M., & Mihaylova, L. S. (2020) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-019-00097-8 · Cited by 4

Advancing Air Combat Tactics with Improved Neural Fictitious Self-play Reinforcement Learning

He, S., Gao, Y., Zhang, B., Chang, H. Y., & Zhang, X. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-4761-4_55 · Cited by 3

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Hu, Z., Liang, X., Zhang, J., Xiaochuan, Y. O. U., & Chengcheng, M. A. (2025) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2025.103663 · Cited by 3

Development of a Genetic Fuzzy Controller and Its Application to a Noisy Inverted Double Pendulum

Sathyan, A., & Cohen, K. D. O. (2018) · InTech eBooks · doi:10.5772/intechopen.78786 · Cited by 3

Research on Architecture of Intelligent Command and Control System

Su, B., Zhao, H., Qi, T., Liu, X., & Yu, R. (2019) · International Conference on Virtual Reality and Intelligent Systems (ICVRIS) · doi:10.1109/icvris.2019.00094 · Cited by 3

Threat Assessment for Rotte Based on Cooperative Tactical Recognition

Meng, G., Zhou, M., Zhang, H., & Sun, D. (2019) · International Conference on Ubiquitous Computing and Communications (IUCC) · doi:10.1109/iucc/dsci/smartcns.2019.00107 · Cited by 3

Composite

Tahir, H., Saad, M., Shafi, N., & Muslim, F. (2021) · Interface science and technology · doi:10.1016/b978-0-12-818805-7.00004-7 · Cited by 3

Implementation of Artificial Intelligence on Indonesia’s Defense Intelligence Activities

Ramadhianto, R., Toruan, T. S. L., Kertopati, S. N. H., & Almubaroq, H. Z. (2023) · Jurnal Pertahanan Media Informasi tentang Kajian dan Strategi Pertahanan yang Mengedepankan Identity Nasionalism dan Integrity · doi:10.33172/jp.v9i2.14657 · Cited by 3

The Autonomy Evolution in Unmanned Aerial Vehicle: Theory, Challenges and Techniques

Eltabey, M. M., Mawgoud, A. A., & Abu-Talleb, A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-58669-0_47 · Cited by 3

A Data Fusion System for Simulation of Critical Scenarios and Decision-Making

Zuluaga, J. A. F., Carrasco, E. P., Pabón, J. D. O., Leon, K. G., & Montoya, O. L. Q. (2019) · Ciencia e Ingeniería Neogranadina · doi:10.18359/rcin.4131 · Cited by 3

A hierarchical reinforcement learning method on multi UCAV air combat

Wang, Y., Jiang, T., & Li, Y. (2021) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1117/12.2615268 · Cited by 3

Genetic Fuzzy Methodology to Predict Time to Return to Play from Sports-Related Concussion

Sathyan, A., Yuan, W., Fleck, D. E., Bonnette, S., Diekfuss, J. A., Martis, M., Gable, A. D., Myer, G. D., Altaye, M., Dudley, J. A., et al. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82099-2_34 · Cited by 3

Multi‐Agent Game Strategies for Kill Chain Optimization in Networked Aerial Combat Systems

Ye, H., Chi, C., Qin, G., & Kuang, Y. (2025) · IET Control Theory and Applications · doi:10.1049/cth2.70064 · Cited by 2

Collaborative strategy for hybrid actions of radar modes and maneuver decisions under observation errors

Wang, X., Yang, Z. C., Huang, J., Zhang, B., Zhang, Y., & Zhou, D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111774 · Cited by 2

Air combat joint strategy learning based on a dual-loop framework and hindsight experience replay

Zhang, Y., Yang, Z., Zhang, B., Wang, X., Piao, H., & Zhou, D. (2026) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwag006 · Cited by 2

Can Physically-Trained Genetic Fuzzy Learning Algorithm Improve Pitch Control in Wind Turbines?

Sathyan, A., Eisa, S. A. N., & Cohen, K. D. O. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16038-7_24 · Cited by 2

Research on Hybrid Intelligence Wargame Method

Jin, X., Wang, X., Ding, R., & Wu, Y. (2022) · IEEE International Conference on Computer and Communication Engineering Technology (CCET) · doi:10.1109/ccet55412.2022.9906386 · Cited by 2

A Data-Driven Method for Estimating Formation Flexibility in Beyond-Visual-Range Air Combat

Scukins, E., Costa, A. N., & Ögren, P. (2024) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas60882.2024.10557091 · Cited by 2

Vehicle Design in Aerial Robotics

Butler, C., & Costello, M. (2021) · Current Robotics Reports · doi:10.1007/s43154-021-00069-y · Cited by 2

The Development of Rule-based AI Engagement Model for Air-to-Air Combat Simulation

Lee, M.-S., Oh, J., Kim, C., Bae, J., Kim, Y.-D., & Jee, C. (2022) · Journal of the Korea Institute of Military Science and Technology · doi:10.9766/kimst.2022.25.6.637 · Cited by 2

One-versus-one air combat algorithm considering direction of the lift vector

Lee, B.-Y., Han, S., Park, H., & Tahk, M.-J. (2017) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med.2017.7984129 · Cited by 2

Simulation Game System: A Possible Way to Realize Intelligent Command and Control

Jin, X. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-7605-3_92 · Cited by 2

Optimal Guidance Method for UCAV in Close Free Air Combat

Chen, Y., Sun, X., Liu, D., & Li, S. (2019) · International Conference on Ubiquitous Computing and Communications (IUCC) · doi:10.1109/iucc/dsci/smartcns.2019.00086 · Cited by 2

Comparing the Explainability and Performance of Reinforcement Learning and Genetic Fuzzy Systems for Safe Satellite Docking

Dunlap, K. M., Cohen, K. D. O., & Hobbs, K. L. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82099-2_11 · Cited by 2

Strategy Generation Based on DDPG with Prioritized Experience Replay for UCAV

Lu, J., Zhao, Y., Kang, Y., Wang, Y., & Deng, Y. (2022) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm54641.2022.9959220 · Cited by 2

Aircraft Maneuver Intelligent Recognition: An End-to-end Regularized Autoencoder and SVM Approach

Yue, L., Yang, R., Zuo, J., Zhang, Y., Zhao, X., & Yan, M. (2022) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus55513.2022.9987228 · Cited by 2

Silahlı İnsansız Hava Araçlarına İlişkin Bilimsel Yayınların Bibliyometrik Analizi

Aydın, N. (2024) · Dumlupınar Üniversitesi Sosyal Bilimler Dergisi · doi:10.51290/dpusbe.1455380 · Cited by 2

Warfare and Machines: An In-depth Study of Autonomous Weapons in the Context of International Humanitarian Law

Ahmad, M., & Anwar, A. (2023) · Society Law and Policy Review · doi:10.62585/slpr.v2i1.25 · Cited by 2

Autonomous decision-making method for UAV formations with dynamically switchable targets for open environments

Wu, Y., Chen, W., Peng, L., Zhang, W., Wang, M., & Wang, G. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.111048 · Cited by 1

Optimizing AI Education for Stakeholders: Leveraging the Bayesian Optimization Framework for Enhanced Learning

Bhardwaj, S., Seth, K., Verma, R., Kumar, V. M., Bhanu, D., & Mithun Kumar, S. (2025) · International Conference on Metaverse and Current Trends in Computing (ICMCTC) · doi:10.1109/icmctc62214.2025.11196189 · Cited by 1

Adaptive Multi-Agent Control Strategy in Heterogeneous Countermeasure Environments

Wang, W., Liu, H., & Lin, W. (2021) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.2021040103 · Cited by 1

FORMASI KEKUASAAN BIROKRAT GARIS DEPAN DALAM IMPLEMENTASI PROGRAM BANTUAN STIMULA PERUMAHAN SWADAYA BAGI MASYARAKAT BERPENGHASILAN RENDAH DI KABUPATEN MANGGARAI TIMUR

Ethelbert, Y. K., Margono, S. A., & Pratama, M. R. (2022) · Jurnal Mediasosian Jurnal Ilmu Sosial dan Administrasi Negara · doi:10.30737/mediasosian.v6i1.2368 · Cited by 1

An Off-COMA Algorithm for Multi-UCAV Intelligent Combat Decision-Making

Shi, Z., Wang, J., & Wang, H. (2022) · International Conference on Data-driven Optimization of Complex Systems (DOCS) · doi:10.1109/docs55193.2022.9967776 · Cited by 1

Accurate Decision-Making Method for Air Combat Pilots Based on Data-Driven

Mao, Y., Xia, Z., Li, Q., He, J., & Fei, A. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-8991-9_31 · Cited by 1

A methodology for representing and assessing artificial intelligence decision aids within modeling and simulation

Wong, J., Nack, E. A., Steelman, Z. R., Erway, S., & Bastian, N. D. (2024) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.3013180 · Cited by 1

Research on Autonomous Manoeuvre Decision Making in Within-Visual-Range Aerial Two-Player Zero-Sum Games Based on Deep Reinforcement Learning

Lu, B., Ru, L., Hu, S., Wang, W., Xi, H., & Zhao, X. (2024) · Mathematics · doi:10.3390/math12142160 · Cited by 1

Autonomous Maneuver Decision-Making for Unmanned Combat Aerial Vehicle Based on Modified Marine Predator Algorithm and Fuzzy Inference

Luo, Y., Ding, D., Tan, M., Liu, Y., Li, N., Zhou, H., & Wang, F. (2025) · Drones · doi:10.3390/drones9040252 · Cited by 1

Un modelo interdisciplinario para la macroeconomía

Haldane, A. G., & Turrell, A. (2018) · Revista de Economía Institucional · doi:10.18601/01245996.v21n40.03 · Cited by 1

Increasing relative angular velocity for air combat in zero gravity

Deng, B., & Collier, T. (2019) · Aerospace Systems · doi:10.1007/s42401-019-00030-0 · Cited by 1

Technical Direction Analysis on Multi-Branch Large-Sample Wargaming: A Hybrid Intelligence Based Solution to Future Command & Control

Jin, X., Xu, T., Zhou, F., & Wu, Y. (2019) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2019.10169 · Cited by 1

Fusion Framework: Research Testbed for Human-Autonomy Teaming

Rowe, A., Spriggs, S., Boyer, J., & Hughes, T. (2018) · Cited by 1

Modeling and Simulation on One-vs-One Air Combat with Deep Reinforcement Learning

Moon, I., Jung, M.-J., & Kim, K. (2020) · Journal of the Korea Society for Simulation · doi:10.9709/jkss.2020.29.1.039 · Cited by 1

Formal Verification of a Genetic Fuzzy System for Unmanned Aerial Vehicle Navigation and Target Capture in a Safety Corridor

Arnett, T. J., Ernest, N. D., Kunkel, B., & Boronat, H. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-81561-5_30 · Cited by 1

Lightweight and heavyweight technologies for autonomous vehicles: A survey

Sharma, K., Naeem, U., Krishnamurthi, R., & Kumar, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90592-3.00001-x · Cited by 1

Pulsar Candidate Selection Using a Genetic Fuzzy System

Verbryke, M. R., & Cohen, K. D. O. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16038-7_28 · Cited by 1

FuzzyPene: a Data-driven Genetic Fuzzy Tree Approach for Agent Safe Penetrations

Zhou, X., Li, K., Zhang, X., Zhang, X., & Zhu, K. (2022) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm54641.2022.9959252 · Cited by 1

Generation and Simulation of Basic Maneuver Action Library for 6-DOF Aircraft by Reinforcement Learning

Wang, J., Teng, J., He, Y., Yang, H., Ji, Y., Tang, Z., & Bai, N. (2022) · Journal on internet of things · doi:10.32604/jiot.2022.031043 · Cited by 1

Artificially intelligent warfare and the revolution in military affairs

ijebor, C. (2020) · Mspace (University of Manitoba) · Cited by 1

Adaptive Simulation-based Training of AI Decision-makers using Bayesian OptimizationPreprint

Israelsen, B., Ahmed, N. R., Center, K., Green, R., & Bennett, W. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.09310

Genetic Fuzzy System in Weapon Control Module of Unmanned Tanks

Liu, X., Ding, D., Yu, Z., Wang, Z., Dai, Y., & Jing, H. (2018) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/cmsa-18.2018.69

Robot Confrontation Based On Genetic Fuzzy System Guided Deep Deterministic Policy Gradient Algorithm

Gu, M., Guo, X., & Zhang, X. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9326610

Takagi-Sugeno Fuzzy Systems with Triangular Membership Functions as Interpretable Neural Networks

Bede, B., & Williams, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82099-2_2

Design of Large-Scale Networked Multi-Agent Control Software Framework for Complex Equipments

Wu, H. Y., Yu, B., An, B., & Fan, H. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9601854

A Dynamic Hierarchical Genetic-Fuzzy Sugeno Network

Macmann, O. B., & Cohen, K. D. O. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-81561-5_27

Predictive Maintenance of Aircraft Engines Using Fuzzy Bolt$$^{\copyright }$$

Mayadevi, B., Martis, D., Sathyan, A., & Cohen, K. D. O. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-81561-5_11

Autonomous Operation (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-007

Centralized Control/Decentralized Execution (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-003

In Extremis (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-005

Armistice (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-011

Vital Ground (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-008

Intelligence, Surveillance, and Reconnaissance (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-006

References

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-013

Unidentified Flying Objects (USAF)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-010

Hostile Environment (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-004

Escalation (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-009

Identification Friend or Foe (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-002

Event Matrix (DoD)

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-001

Notes

(2020) · Duke University Press eBooks · doi:10.1215/9781478007272-012

A Station Guidance Method Based on Combination Deviation Spacecraft Orbit Prediction

Yufan, H., Jingqi, H., Li, J., & Ning, Z. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-3387-5_52

The Behavioral Responses of the Employees and the Ways of Improvement While Facing Changes in the Organization

HOVHANNISYAN, A., & Simonyan, A. R. (2023) · Messenger of Armenian State University of Economics · doi:10.52174/1829-0280_2023.1-26

Genetic Fuzzy Threat Assessment for Asteroids 2600 Derived Game

Heitmeyer, D., & Cohen, K. D. O. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-46778-3_8

A Fuzzy Inference System for an Optimal Spacecraft Attitude State Trajectory

Walker, A. R. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-46778-3_25

Review of Applications of Artificial Intelligence in Health Care

Memon, B. (2024) · Sukkur IBA Journal of Computing and Mathematical Sciences · doi:10.30537/sjcms.v7i2.1308

An Improved DRL Algorithm for Reliable Path Planning

Cheng, C., Zhou, L., Wei, Q., Jin, Y. J., Leng, J., & Ding, X. (2023) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc59353.2023.10400305

Event-Triggered Attitude Controller Design for Unmanned Aerial Vehicles under Cyber Attacks

Han, S., Guo, X., Jin, Y., Lim, Y., & Kommuri, S. K. (2024) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie54533.2024.10595751

Continuous Action Air Combat Maneuver Decision-Making Based on T-MGMM

Jiang, J., Wang, H., Huang, Z., Zhou, Z., Wu, X., Deng, W., & Chen, X. (2024) · IEEE Access · doi:10.1109/access.2024.3509215

Fuzzy AI for Evaluating Relative Desirability in Autonomous Vehicle Navigation

Firos, A., & Khanum, S. (2024) · Advances in mechatronics and mechanical engineering (AMME) book series · doi:10.4018/979-8-3693-5797-2.ch012

The Simulation of Mosaic Air Combat Based on Agent and Behavior Tree

Weisheng, S., Wenyu, Z., & Zhang, Y. (2024) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia63733.2024.10808581

SOTM: A Simulation-Oriented Trustworthiness Measurement Method for Intelligent Unmanned System

Jiang, M., Gong, Y., Yang, G., Meng, L., & Ma, Y. (2025) · International Symposium on System Security, Safety, and Reliability (ISSSR) · doi:10.1109/isssr65654.2025.00040

HPFIA: A High-Performance Fuzzy Inference Accelerator for Situation Assessment on Airborne Equipment

Gao, L., Jiang, J., & Xu, J. (2024) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc64274.2024.00068

Hybrid Attack Decision Making with the Introduction of Radar Operating Modes

Wang, X., Zhang, B., Zhou, Y., & Zhou, D. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11179081

Deep Reinforcement Learning-Based Trajectory Planning for Aircraft Under Multi-Agent Engagement Scenario

Yu, H., Zong, Q., Zhang, X., Liu, D., & Zhang, R. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11178581

Multi-Agent Behavior Cloning for Modeling Realistic Adversaries in Air Combat Simulations

Strand, A., Bråthen, K., Asprusten, M., & Gorton, P. (2025) · International Conference on Machine Learning and Applications · doi:10.1109/icmla66185.2025.00013

Application of Intelligent Gaming in Command and Control Strategies

Yang, J., Fubiao, X., Han, Y., Li, H., & Bingchen, C. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11487927

Application of Self-Play Reinforcement Learning and Explainable Decision Tree in Intelligent Air Combat

Wang, J., Zhu, L., Xia, S., Liu, H., Liu, J., Qu, C., & Song, Z. (2026) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2026.000079

Genetic Fuzzy Tree-Based Routing for Sail Plane in Atmospheric Waves

Arnett, T. J., Phillips, Z., & Ernest, N. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-35459-4_37

Genetic Fuzzy Trees for Intelligent Control of Unmanned Combat Aerial Vehicles

Ernest, N. D. (2015) · OhioLink ETD Center (Ohio Library and Information Network) · doi:10.13140/rg.2.2.13052.54402 · Cited by 26

Genetic Fuzzy Trees and their Application Towards Autonomous Training and Control of a Squadron of Unmanned Combat Aerial Vehicles

Ernest, N. D., Cohen, K. D. O., Kivelevitch, E. H., Schumacher, C. J., & Casbeer, D. W. (2015) · Unmanned Systems · doi:10.1142/s2301385015500120 · Cited by 72

Development of a Genetic Fuzzy Controller for an Unmanned Aerial Vehicle

Qu, Y., Pandhiti, S., Bullard, K. S., Potter, W. D., & Fezer, K. F. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21827-9_34 · Cited by 3

Learning of Intelligent Controllers for Autonomous Unmanned Combat Aerial Vehicles by Genetic Cascading Fuzzy Methods

Ernest, N. D., Cohen, K. D. O., Schumacher, C. J., & Casbeer, D. W. (2014) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2014-01-2174 · Cited by 17

Optimization of Fuzzy Controller for an Uninhabited Airplane Using Genetic Algorithms

Perhinschi, M. G. (2002) · PAMM · doi:10.1002/1617-7061(200203)1:1 · Cited by 3