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Technical Trends of AI Military Staff to Support Decision-Making of Commanders

지휘관들의 의사결정지원을 위한 AI 군참모 기술동향

  • Published : 2021.02.01

Abstract

The Ministry of National Defense aims to create an environment in which transparent and reasonable defense policies can be implemented in real time by establishing the vision of smart defense innovation based on the Fourth Industrial Revolution and promoting innovation in technology-based defense operation systems. Artificial intelligence (AI) based defense technology is at the level of basic research worldwide, includes no domestic tasks, and involves classified military operation data and command control/decision information. Further, it is needed to secure independent technologies specialized for our military. In the army, military power continues to decline due to aging and declining population. In addition, it is expected that there will be more than 500,000 units should be managed simultaneously, to recognize the battle situation in real time on the future battlefields. Such a complex battlefield, command decisions will be limited by the experience and expertise of individual commanders. Accordingly, the study of AI core technologies supporting real-time combat command is actively pursued at home and abroad. It is necessary to strengthen future defense capabilities by identifying potential threats that commanders are likely to miss, improving the viability of the combat system, ensuring smart commanders always win conflicts and providing reasonable AI digital staff based on data science. This paper describes the recent research trends in AI military staff technology supporting commander decision-making, broken down into five key areas.

Keywords

References

  1. 한국전자통신연구원,"지능정보사회로 가는 길: 기술발전지도 2035," 2020. 6.
  2. J. Weiss, "The army's ultimate heads-up display: The ivas," Nov. 5, 2020, https://sofrep.com/news/the-armys-ultimate-heads-up-display-the-ivas/
  3. A. Sadeghian et al., "Drum: End-to-end differentiable rule mning on knowledge graphs," in Proc. Advan. Neural. Inform. Process. Syst. 2019.
  4. K. Sun et al., "Top K hypotheses selection on a knowledge graph," in Proc. Int. Flairs. Conf. Sarasota, FL, USA, May. 2019, pp. 136-139.
  5. H. Paulheim, "Knowledge graph refinement: A survey of approaches and evaluation methods," Semantic Web. vol. 8 no. 3, 2017 pp. 489-508. https://doi.org/10.3233/SW-160218
  6. DARPA, "Active interpretation of disparate alternatives (AIDA)," "DARPA Wades into Murky Multimedia Information Streams to Catch Big Meaning,"https://www.darpa.mil/news-events/2017-04-06DARPA
  7. U.S. National Institute of Standards and Technology (NIST), "Streaming multimedia knowledge base population (SM-KBP) track,", in Proc. Text. Analysis. Conf. Nov. 2019, https://tac.nist.gov/2019/SM-KBP/index.html
  8. L. Manling et al. "Gaia: a fine-grained multimedia knowledge extraction system," in Proc. Assoc. Comput. Linguist.: Syst. Demonstrations. July. 2020, pp. 77-86.
  9. DARPA Announces COMPASS Programme, shephardmedia. com, 2018. 3. 21.
  10. DARPA, "DARPA demonstrates "Competition" tool at combatant command," 2020. 3. 19. https://www.darpa.mil/news-events/2020-03-19a
  11. J. R. Surdu and K. Kittka, "Deep green: Commander's tool for COA's concept," in Proc. Int. Conf. Comput. Commun. Control. Technol. Orlando, FL, USA, June. 2008.
  12. R. Cieslak, "GIG 3.0 Design Factors," Public Intelligence, http://info.publicintelligence.net/USPACOMGIG.pdf
  13. CISCO, "Realizing the value of the IoT where we work, live, play and learn in defense," Oct. 2017.
  14. US Department of Defense, "DoD digital modernization strategy," July. 2019, https://media.defense.gov/2019/Jul/12/2002156622/-1/-1/1/DOD-DIGITAL-MODERNIZATION-STRATEGY-2019.PDF