국방획득체계와 연계한 국방 인공지능(AI) 체계 시험평가 방안
Test and Evaluation Procedures of Defense AI System linked to the ROK Defense Acquisition System
0
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
In this research, a new Test and Evaluation (T&E) procedure for defense AI systems is proposed to fill the existing gap in established methodologies. This proposed concept incorporates a data-based performance evaluation, allowing for independent assessment of AI model efficacy. It then follows with an on-site T&E using the actual AI system. The performance evaluation approach adopts the project promotion framework from the defense acquisition system, outlining 10 steps for R&D projects and 9 steps for procurement projects. This procedure was crafted after examining AI system testing standards and guidelines from both domestic and international civilian sectors. The validity of each step in the procedure was confirmed using real-world data. This study's findings aim to offer insightful guidance in defense T&E, particularly in developing robust T&E procedures for defense AI systems.
Abstract
In this research, a new Test and Evaluation (T&E) procedure for defense AI systems is proposed to fill the existing gap in established methodologies. This proposed concept incorporates a data-based performance evaluation, allowing for independent assessment of AI model efficacy. It then follows with an on-site T&E using the actual AI system. The performance evaluation approach adopts the project promotion framework from the defense acquisition system, outlining 10 steps for R&D projects and 9 steps for procurement projects. This procedure was crafted after examining AI system testing standards and guidelines from both domestic and international civilian sectors. The validity of each step in the procedure was confirmed using real-world data. This study's findings aim to offer insightful guidance in defense T&E, particularly in developing robust T&E procedures for defense AI systems.
참고문헌 (30)
- Calabrese, J., "Framework for Data Quality Evaluation Based on ISO", IEC 25012and ISO, VIII Conference on Cloud Computing, Big Data & Emerging Topics (Modalidad virtual, 8 al 10 de septiembre de 2020), pp.2020[DOI]
- Chang, W., "ISO", IEC JTC 1, WG 2 (data)data quality for analytics and machine learning (ML), pp.Information Technology Laboratory, 2022[DOI]
- 조규태, "M&S 기반 시험평가 장비 개발의 효율성 및 신뢰성 강화 방안", 한국시뮬레이션학회 논문지, 21(1), pp.89~96 (2012), 한국시뮬레이션학회
- 조규표, "국방분야 인공지능 영상감시체계의 시험평가 발전방안 연구", 신뢰성 응용연구, 23(1), pp.97~104 (2023), 한국신뢰성학회[DOI]
- 변재현, "자율무기체계 지능화를 위한 시뮬레이션 기반 강화학습", 한국시뮬레이션학회 논문지, 32(2), pp.91~111 (2023), 한국시뮬레이션학회[DOI]
- Clegg, A., "Sim2real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?", IEEE Robotics and Automation Letters, 5(4), pp.6670~6677 (2020)
- Géron, A., "Hands-On Machine Learning with Scikit-Learn", pp.61~ (2020), Hanbit Publishing Network
- "ISO", IEC JTC 1, WG2, 《ISO, pp.IEC WD –5259-5:202X(E)(Artificial intelligence-Data quality for analytics and ma-chine learning(ML)-Part 5: Data quality governance framework≫[DOI]
- "ISO", IEC JTC 1, WG2, 《ISO, pp.IEC WD 5259-1:202X(E)(Artificial intelligence-Data quality for analytics and ma-chine learning(ML)-Part 1: Overview, terminology, and example≫[DOI]
- "ISO", IEC JTC 1, WG2, 《ISO, pp.IEC WD 5259-2:202X(X)(Artificial intelligence-Data quality for analytics and ma-chine learning(ML)-Part 2: Data quality measures≫[DOI]
- "ISO", IEC JTC 1, WG2, 《ISO, pp.IEC WD 5259-3:20##(X)(Artificial intelligence-Data quality for analytics and ma-chine learning(ML)-Part 3: Data quality management requirements and guidelines≫[DOI]
- "ISO", IEC JTC 1, WG2, 《ISO, pp.IEC WD 5259-4:202#(E)(Artificial intelligence-Data quality for analytics and ma-chine learning(ML)-Part 4: Data quality process frame-work≫[DOI]
- "ISO", IEC JTC 1, IEC 25024:2015 Systems and software engineering: Systems and software Quality Requirements and Evaluation (SQuaRE) - Measurement of data quality≫, 2015[DOI]
- Kim, B. -S., "A study on the test evaluation of automated-vehicles based on artificial intelligence", Spring Conf, pp.722~722 (2019)
- Kim, B. S., "A Study on the Test Evaluation of Automated-Vehicles Based on Artificial Intelligence", The Korean Society of Automotive Engineers, Annual Spring Conference, pp.722~722 (2019)
- Kim, M. Y., "Acquisition Process of Military Weapon System Using Artificial Intelligence", Proceed-ings of Symposium of the Korean Institute of Communica-tions and Information Sciences (2022)
- Kohavi, R., "A study of cross-validation and bootstrap for accuracy estimation and model selection", Proc. 14th Int. Joint Conf. on Artif. Intell., pp.1137~1143 (1995)
- Kurrek, P., "Ai motion control–a generic approach to develop control policies for robotic manipulation tasks", Proceed-ings of the Design Society: International Conference on Engineering Design, 1(1) (2019), Cambridge University Press
- 이용복, "인공지능(AI) 무기체계 시험평가방법 발전방안 연구: 분류모델 성능평가를 중심으로", 신뢰성 응용연구, 22(1), pp.1~9 (2022), 한국신뢰성학회[DOI]
- Trustworthy Artificial Intelligence Development Guide (Plan), "Ministry of Science and ICT", ~ (2023)
- National Information Society Agency, "Republic of Korea: Guide for Constructing Artificial Intelligence Training Data", ~ (2023)
- 박주혜, "시험평가용 M&S에 대한 V&V 프로세스 연구", 한국산학기술학회논문지, 20(9), pp.397~404 (2019), 한국산학기술학회[DOI]
- Test & evaluation of ai-en-abled and autonomous systems: A literature review, "Porter, D. J.", Institute for Defense Analyses, ~ (2020)
- Priestley, M., "A survey of data quality requirements that matter in ML develop-ment pipelines", ACM Journal of Data and Information Quality, 15(2), pp.1~39 (2023)
- Rangineni, S., "An Analysis of Data Quality Requirements for Machine Learning Development Pipelines Frame-works", International Journal of Computer Trends and Technology, 71(9), pp.16~27 (2023)
- Schnelle, S., "ADS Standardization Landscape: Making Sense of its Status and of the Associated Research Questions", arXiv preprint arXiv, 2023, 2306.17682, ~ (2023)
- Shin, J. H., "Data quality verification method for artificial intelligence learning", Journal of Electronic Engineering, 48(7), pp.28~34 (2021)
- Torchiano, M., "Preserving the benefits of Open Government Data by measuring and improving their quality: An Empirical Study", IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), 1 (2017), IEEE
