Genetic Fuzzy based Artificial Intelligence for Unmanned Combat Aerial Vehicle Control in Simulated Air Combat Missions
Open accessAbstract
Breakthroughs in genetic fuzzy systems, most notably the development of the Genetic Fuzzy Tree methodology, have allowed fuzzy logic based Artificial Intelligences to be developed that can be applied to incredibly complex problems. The ability to have extreme performance and computational efficiency as well as to be robust to uncertainties and randomness, adaptable to changing scenarios, verified and validated to follow safety specifications and operating doctrines via formal methods, and easily designed and implemented are just some of the strengths that this type of control brings. Within this white paper, the authors introduce ALPHA, an Artificial Intelligence that controls flights of Unmanned Combat Aerial Vehicles in aerial combat missions within an extreme-fidelity simulation environment. To this day, this represents the most complex application of a fuzzy-logic based Artificial Intelligence to an Unmanned Combat Aerial Vehicle control problem. While development is on-going, the version of ALPHA presented withinwas assessed by Colonel (retired)Gene Lee who described ALPHA as “the most aggressive, responsive, dynamic and credible AI (he’s) seen-to-date.” The quality of these preliminary results in a problem that is not only complex and rife with uncertainties but also contains an intelligent and unrestricted hostile force has significant implications for this type of Artificial Intelligence. This work adds immensely to the body of evidence that this methodology is an ide…
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Military use of AI in cyber warfare or regarding weaponized unmanned vehicles or drones is a leading area of unwanted side effects and malevolent contexts of AI use.
“Leading, of course, is the military use of AI in cyber warfare or regarding weaponized unmanned vehicles or drones (Ernest and Carroll 2016; Anderson and Waxman 2013).”
From The Ethics of AI Ethics: An Evaluation of Guidelines (2020) · cites this paper as Ernest and Carroll 2016
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- Authors
- Ernest, N., & Carroll, D.
- Venue
- Journal of Defense Management
- Publisher
- OMICS Publishing Group
- Published
- 2016
- Volume
- 06
- Issue
- 01
- ISSN
- 2167-0374 (ISSN-L)
- OpenAlex
- W2518892488
- Cited by
- 185
- References
- 0
- Citations by year
- 2026: 3 · 2025: 21 · 2024: 20 · 2023: 15 · 2022: 31 · 2021: 22 · 2020: 41 · 2019: 15 · 2018: 9 · 2017: 8
- FWCI
- 8.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Journal of Defense Management (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Aerospace Engineering and Applications
- Physical Sciences › Engineering › Aerospace Engineering › Aerospace Engineering and Control Systems
- Keywords
- genetic fuzzy systems, unmanned combat aerial vehicles, UAV flight control, air combat simulation, autonomous aircraft, high-fidelity simulation, robustness to uncertainty
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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