Expediting the Targeting Process Responsibly using Artificial Intelligence

被引:0
|
作者
Lowrance, Christopher J. [1 ]
Harris, Elliott R. [1 ]
Pfaff, C. Anthony [1 ]
机构
[1] US Army War Coll, 47 Ashburn Dr, Carlisle, PA 17013 USA
来源
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS APPLICATIONS IV | 2022年 / 12113卷
关键词
AI-enabled targeting process; military artificial intelligence; Department of Defense modernization; fires warfighting function; fuzzy control and decision making;
D O I
10.1117/12.2619098
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Absent the use of artificial intelligence (AI), each stage of the targeting process requires human input, which consumes time - a precious resource during high-intensity conflict. To counter that problem, the application of AI and related technologies could rapidly automate certain steps within the targeting process. The challenge, however, becomes how to responsibly speed up the process using AI, while also maintaining appropriate levels of human oversight based on level of risk acceptable to the commander. We address this challenge with the introduction of a fuzzy logic controller that is designed to automatically adapt and optimize the level of human-machine interaction based on the current targeting conditions. The logic controller uses the confidence of the AI algorithm at each stage of the targeting process, as well as the commander's risk tolerance at the time, to automatically determine what stages of the targeting process can be trusted for AI to accelerate and which ones require explicit human verification. The final stage of verifying and authorizing a fire mission is reserved for humans only to make.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Artificial intelligence application in the bending process
    Groze, Florica Mioara
    Achimas, Gheorghe
    Lazarescu, Lucian
    Ceclan, Vasile
    ANNALS OF DAAAM FOR 2007 & PROCEEDINGS OF THE 18TH INTERNATIONAL DAAAM SYMPOSIUM: INTELLIGENT MANUFACTURING & AUTOMATION: FOCUS ON CREATIVITY, RESPONSIBILITY, AND ETHICS OF ENGINEERS, 2007, : 307 - 308
  • [42] Artificial intelligence and editorial process in CSP
    Alves, Luciana Correia
    de Lima, Luciana Dias
    Carvalho, Marilia Sa
    CADERNOS DE SAUDE PUBLICA, 2024, 40 (11):
  • [43] ARTIFICIAL-INTELLIGENCE AND PROCESS SIMULATION
    BIONDO, SJ
    ISA TRANSACTIONS, 1992, 31 (02) : 39 - 47
  • [44] Distributed artificial intelligence in process control
    Chu, E
    Srihari, K
    Emerson, CR
    COMPUTERS & INDUSTRIAL ENGINEERING, 1996, 31 (1-2) : 397 - 400
  • [45] ARTIFICIAL INTELLIGENCE IN PROCESS ENGINEERING.
    Venkatasubramanian, V.
    Chemical Engineering Education, 1986, 20 (04): : 188 - 193
  • [46] Is artificial intelligence improving the audit process?
    Fedyk, Anastassia
    Hodson, James
    Khimich, Natalya
    Fedyk, Tatiana
    REVIEW OF ACCOUNTING STUDIES, 2022, 27 (03) : 938 - 985
  • [47] Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?
    Arunthavanathan, Rajeevan
    Sajid, Zaman
    Amin, Md. Tanjin
    Tian, Yuhe
    Khan, Faisal
    Pistikopoulos, Efstratios
    AICHE JOURNAL, 2024, 70 (07)
  • [48] Using thermography responsibly
    Lawson, Joseph R.
    CANADIAN MEDICAL ASSOCIATION JOURNAL, 2017, 189 (27) : E917 - E917
  • [49] Expediting the preclinical development process
    Logan, C
    DRUG DISCOVERY TODAY, 1999, 4 (10) : 452 - 453
  • [50] Artificial Intelligence in the Assessment Process of MOOCs using a cloud-computing ecosystem
    Reategui, Jose L.
    Herrera, Pablo C.
    IEEE TALE2021: IEEE INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY AND EDUCATION, 2021, : 487 - 493