High-Tech Defense Industries: Developing Autonomous Intelligent Systems
Abstract
:1. Introduction
2. Conceptual Background
2.1. Concepts and Definitions
2.2. Type and Levels of Autonomy
2.3. Military Applications of Autonomous Intelligent Systems
- Space robotics and autonomous intelligent systems;
- Autonomous intelligent cyber-defense agents;
- Intelligent unmanned autonomous systems—in the air, at sea, and on land.
3. Methodology
4. Results
Three Modes of Autonomous Intelligent Systems in the Defense Industry
- Mode 1. Fully autonomous operation (the human has no control over the operation).
- Mode 2. Partially autonomous operation (the human has some kind of control over the operation, or the system assists humans and vice-versa).
- Mode 3. Smart autonomous decision-making (the intelligent system supports human decision).
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Lin, P.; Bekey, G.; Abney, K. Autonomous Military Robotics: Risk, Ethics, and Design. California Polytechnic State University of San Luis Obispo. 2008. Available online: https://apps.dtic.mil/sti/pdfs/ADA534697.pdf (accessed on 14 April 2021).
- Ha, Q.; Yen, L.; Balaguer, C. Robotic autonomous systems for earthmoving in military applications. Autom. Constr. 2019, 107, 102934. [Google Scholar] [CrossRef]
- Mori, S. US defense innovation and artificial intelligence. Asia-Pac. Rev. 2018, 25, 16–44. [Google Scholar] [CrossRef]
- Mariani, J.; Williams, B.; Loubert, B. Continuing the March: The Past, Present, and Future of the IoT in the Military. The Internet of Things in Defense. Technical Report. 2015. Available online: https://www2.deloitte.com/us/en/insights/focus/internet-of-things/iot-in-military-defense-industry.html (accessed on 14 April 2021).
- Payal, M.; Dixit, P.; Sairam, T.; Goyal, N. Robotics, AI, and the IoT in Defense Systems. In AI and IoT-Based Intelligent Automation in Robotics; Wiley-Scrivener: Hoboken, NJ, USA, 2021. [Google Scholar]
- Rossiter, A. Bots on the ground: An impending UGV revolution in military affairs? Small Wars Insur. 2020, 31, 851–873. [Google Scholar] [CrossRef]
- Tilford, E. The Revolution in Military Affairs: Prospects and Cautions. US Army War College. 1995. Available online: https://www.jstor.org/stable/pdf/resrep11803.pdf (accessed on 14 April 2021).
- Zhang, T.; Li, Q.; Zhang, C.; Liang, H.; Li, P.; Wang, T.; Li, S.; Zhu, Y.; Wu, C. Current trends in the development of intelligent unmanned autonomous systems. Front. Inf. Technol. Electron. Eng. 2017, 18, 68–85. [Google Scholar] [CrossRef] [Green Version]
- Yin, R. Case Study Research and Applications: Design and Methods; SAGE Publications: Thousand Oaks, CA, USA, 2018. [Google Scholar]
- Chamola, V.; Kotesh, P.; Agarwal, A.; Gupta, N.; Guizani, M. A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques. Ad. Hoc. Netw. 2020, 111, 102324. [Google Scholar] [CrossRef] [PubMed]
- Šipoš, D.; Gleich, D. A lightweight and low-power UAV-borne ground penetrating radar design for landmine detection. Sensors 2020, 20, 2234. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sapaty, P. Military robotics: Latest trends and spatial grasp solutions. Int. J. Adv. Res. Artif. Intell. 2015, 4, 9–18. [Google Scholar]
- Walsh, G.; Low, D. Military load carriage effects on the gait of military personnel: A systematic review. Appl. Ergon. 2021, 93, 103376. [Google Scholar] [CrossRef]
- Rossiter, A. The impact of robotics and autonomous systems (RAS) across the conflict spectrum. Small Wars Insur. 2020, 31, 691–700. [Google Scholar] [CrossRef]
- Garg, S.; Aujla, G.; Erbad, A.; Rodrigues, J.; Chen, M.; Wang, X. Guest Editorial: Blockchain Envisioned Drones: Realizing 5G-Enabled Flying Automation. IEEE Netw. 2021, 35, 16–19. [Google Scholar] [CrossRef]
- Rathje, J.; Katila, R. Enabling technologies and the role of private firms: A machine learning matching analysis. Strategy Sci. 2021, 6, 5–21. [Google Scholar] [CrossRef]
- Reis, J.; Melão, N.; Salvadorinho, J.; Soares, B.; Rosete, A. Service robots in the hospitality industry: The case of Hennna hotel, Japan. Technol. Soc. 2020, 63, 101423. [Google Scholar] [CrossRef]
- Rosete, A.; Soares, B.; Salvadorinho, J.; Reis, J.; Amorim, M. Service robots in the hospitality industry: An exploratory literature review. In International Conference on Exploring Services Science; Springer: Cham, Switzerland; Porto, Portugal, 2020; pp. 174–186. [Google Scholar]
- Wang, Y.; Hou, M.; Plataniotis, K.; Kwong, S.; Leung, H.; Tunstel, E.; Rudas, I.; Trajkovic, L. Towards a theoretical framework of autonomous systems underpinned by intelligence and systems sciences. IEEE/Caa J. Autom. Sin. 2020, 8, 52–63. [Google Scholar]
- Campbell, S.; O’Mahony, N.; Krpalcova, L.; Riordan, D.; Walsh, J.; Murphy, A.; Ryan, C. Sensor technology in autonomous vehicles: A review. In Proceedings of the 2018 29th Irish Signals and Systems Conference (ISSC), Belfast, UK, 21–22 June 2018; pp. 1–4. [Google Scholar]
- Horowitz, M.C.; Scharre, P.; Velez-Green, A. A Stable Nuclear Future? The Impact of Autonomous Systems and Artificial Intelligence. arXiv 2019, arXiv:1912.05291. Available online: https://arxiv.org/abs/1912.05291 (accessed on 20 April 2021).
- Sandin, P. Robot Mechanisms and Mechanical Devices Illustrated; McGraw Hill Professional: New York, NY, USA, 2003. [Google Scholar]
- Insaurralde, C.; Lane, D. Metric assessment of autonomous capabilities in unmanned maritime vehicles. Eng. Appl. Artif. Intell. 2014, 30, 41–48. [Google Scholar] [CrossRef]
- Schlinger, H. The Myth of Intelligence. Psychol. Rec. 2003, 53, 15–32. [Google Scholar]
- Sternberg, R. The Theory of Successful Intelligence. Interam. J. Psychol. 2005, 39, 189–202. [Google Scholar]
- Huang, M.; Rust, R. Artificial intelligence in service. J. Serv. Res. 2018, 21, 155–172. [Google Scholar] [CrossRef]
- Leidner, D. Cognitive Reasoning for Compliant Robot Manipulation; Springer International Publishing: Cham, Switzerland, 2019. [Google Scholar]
- Ibarz, J.; Tan, J.; Finn, C.; Kalakrishnan, M.; Pastor, P.; Levine, S. How to train your robot with deep reinforcement learning: Lessons we have learned. Int. J. Robot. Res. 2021, 40, 0278364920987859. [Google Scholar] [CrossRef]
- Richards, L.; Matuszek, C. Learning to Understand Non-Categorical Physical Language for Human Robot Interactions. UMBC Student Collection 2016. Available online: http://hdl.handle.net/11603/21316 (accessed on 30 April 2021).
- Huang, M.; Rust, R.; Maksimovic, V. The feeling economy: Managing in the next generation of artificial intelligence (AI). Calif. Manag. Rev. 2019, 61, 43–65. [Google Scholar] [CrossRef]
- Raibert, M.; Blankespoor, K.; Nelson, G.; Playter, R. Bigdog, the rough-terrain quadruped robot. IFAC Proc. Vol. 2008, 41, 10822–10825. [Google Scholar] [CrossRef] [Green Version]
- Murphy, M.; Saunders, A.; Moreira, C.; Rizzi, A.; Raibert, M. The littledog robot. Int. J. Robot. Res. 2011, 30, 145–149. [Google Scholar] [CrossRef]
- Huang, M.; Rust, R. Engaged to a robot? The role of AI in service. J. Serv. Res. 2021, 24, 30–41. [Google Scholar] [CrossRef]
- Taylor, I. Who Is Responsible for Killer Robots? Autonomous Weapons, Group Agency, and the Military-Industrial Complex. J. Appl. Philos. 2020, 38, 320–334. [Google Scholar] [CrossRef]
- Nyholm, S.; Smids, J. Can a robot be a good colleague? Sci. Eng. Ethics 2020, 26, 2169–2188. [Google Scholar] [CrossRef] [PubMed]
- Bellas, A.; Perrin, S.; Malone, B.; Rogers, K.; Lucas, G.; Phillips, E.; Tossel, C.; Visser, E. Rapport building with social robots as a method for improving mission debriefing in human-robot teams. In Proceedings of the 2020 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA, 24 April 2020; pp. 160–163. [Google Scholar]
- Laudon, K.; Laudon, J. Management Information Systems; Pearson: Upper Saddle River, NJ, USA, 2015. [Google Scholar]
- Vagia, M.; Transeth, A.; Fjerdingen, S. A literature review on the levels of automation during the years. What are the different taxonomies that have been proposed? Appl. Ergon. 2016, 53, 190–202. [Google Scholar] [CrossRef] [PubMed]
- Endsley, M.; Kaber, D. Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics 1999, 42, 462–492. [Google Scholar] [CrossRef] [Green Version]
- Giordano, A.; Dietrich, A.; Ott, C.; Albu-Schäffer, A. Coordination of thrusters, reaction wheels, and arm in orbital robots. Robot. Auton. Syst. 2020, 131, 103564. [Google Scholar] [CrossRef]
- Sancho-Pradel, D.; Gao, Y. A survey on terrain assessment techniques for autonomous operation of planetary robots. JBIS-J. Br. Interplanet. Soc. 2010, 63, 206–217. [Google Scholar]
- Gao, Y.; Jones, D.; Ward, R.; Allouis, E.; Kisdi, A. Space Robotics and Autonomous Systems: Widening the Horizon of Space Exploration. UK-RAS White Paper, 2016. Available online: https://www.surrey.ac.uk/sites/default/files/UK_RAS_wp_print_single_pages.pdf (accessed on 23 April 2021).
- Voosen, P. Perseverance will explore history of ancient lake. Science 2021, 371, 870–871. [Google Scholar] [CrossRef]
- Théron, P.; Kott, A. When Autonomous Intelligent Goodware Will Fight Autonomous Intelligent Malware: A Possible Future of Cyber Defense. In Proceedings of the MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM), Norfolk, VA, USA, 12–14 November 2019; pp. 1–7. [Google Scholar]
- Kott, A. Intelligent autonomous agents are key to cyber defense of the future army networks. Cyber Def. Rev. 2018, 3, 57–70. [Google Scholar]
- Campbell, S.; Naeem, W.; Irwin, G. A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres. Annu. Rev. Control 2012, 36, 267–283. [Google Scholar] [CrossRef] [Green Version]
- Santoso, F.; Garratt, M.A.; Anavatti, S. State-of-the-art intelligent flight control systems in unmanned aerial vehicles. IEEE Trans. Autom. Sci. Eng. 2017, 15, 613–627. [Google Scholar] [CrossRef]
- Udeanu, G.; Dobrescu, A.; Oltean, M. Unmanned aerial vehicle in military operations. Sci. Res. Educ. Air Force 2016, 18, 199–206. [Google Scholar] [CrossRef]
- Jourdan, D.; Piedmonte, M.; Gavrilets, V.; Vos, D.; McCormick, J. Enhancing UAV survivability through damage tolerant control. In Proceedings of the AIAA Guidance, Navigation, and Control Conference, Toronto, ON, Canada, 2–5 August 2010; p. 7548. [Google Scholar]
- Stolaroff, J.; Samaras, C.; O’Neill, E.; Lubers, A.; Mitchell, A.; Ceperley, D. Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery. Nat. Commun. 2018, 9, 1–13. [Google Scholar]
- Hartanto, R.; Arkeman, Y.; Hermadi, I.; Sjaf, S.; Kleinke, M. Intelligent Unmanned Aerial Vehicle for Agriculture and Agroindustry. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2019; Volume 335, p. 012001. [Google Scholar]
- Bovio, E.; Cecchi, D.; Baralli, F. Autonomous underwater vehicles for scientific and naval operations. Annu. Rev. Control 2006, 30, 117–130. [Google Scholar] [CrossRef]
- Bistron, M.; Piotrowski, Z. Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens. Electronics 2021, 10, 871. [Google Scholar] [CrossRef]
- Sands, T. Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV). J. Mar. Sci. Eng. 2020, 8, 578. [Google Scholar] [CrossRef]
- Wooden, D.; Malchano, M.; Blankespoor, K.; Howardy, A.; Rizzi, A.; Raibert, M. Autonomous navigation for BigDog. In Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA, 3–7 May 2010; pp. 4736–4741. [Google Scholar]
- Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.; Prisma Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fink, A. Conducting Research Literature Reviews: From the Internet to Paper, 2nd ed.; Sage Publications: Thousand Oaks, CA, USA, 2005. [Google Scholar]
- Tranfield, D.; Denyer, D.; Smart, P. Towards a methodology for developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag. 2003, 14, 207–222. [Google Scholar] [CrossRef]
- Reis, J.; Santo, P.; Melão, N. Influence of artificial intelligence on public employment and its impact on politics: A systematic literature review. Braz. J. Oper. Prod. Manag. 2021, 18, 1–22. [Google Scholar] [CrossRef]
- Liberati, A.; Altman, D.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.; Ioannidis, J.; Clarke, M.; Devereaux, P.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. J. Clin. Epidemiol. 2009, 62, e1–e34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Page, M.; McKenzie, J.; Bossuyt, P.; Boutron, I.; Hoffmann, T.; Mulrow, C.; Shamseer, L.; Telzlaff, J.; Moher, D. Updating guidance for reporting systematic reviews: Development of the PRISMA 2020 statement. J. Clin. Epidemiol. 2021, 134, 103–112. [Google Scholar] [CrossRef]
- Page, M.J.; Moher, D. Evaluations of the uptake and impact of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Statement and extensions: A scoping review. Syst. Rev. 2017, 6, 1–14. [Google Scholar] [CrossRef]
- Flick, U.; Kardorff, E.; Steinke, I. A Companion to Qualitative Research; Sage Publications: Thousand Oaks, CA, USA, 2004. [Google Scholar]
- Neuendorf, K.; Kumar, A. Content analysis: An Introduction to Its Methodology. In The International Encyclopedia of Political Communication; Wiley Online Library: Cleveland, OH, USA, 2015; pp. 1–10. [Google Scholar] [CrossRef]
- Krippendorff, K. Content Analysis: An Introduction to Its Methodology; Sage Publications: Thousand Oaks, CA, USA, 2018. [Google Scholar]
- Stemler, S. An overview of content analysis. Pract. Assess. Res. Eval. 2000, 7, 17. [Google Scholar]
- Elo, S.; Kyngäs, H. The qualitative content analysis process. J. Adv. Nurs. 2008, 62, 107–115. [Google Scholar] [CrossRef]
- Hsieh, H.; Shannon, S. Three approaches to qualitative content analysis. Qual. Health Res. 2005, 15, 1277–1288. [Google Scholar] [CrossRef]
- Bazeley, P.; Jackson, K. Qualitative Data Analysis with NVivo; Sage Publications: Thousand Oaks, CA, USA, 2013. [Google Scholar]
- Hetrick, R. Employment in high-tech defense industries in a post-cold war era. Mon. Labor Rev. 1996, 119, 57. [Google Scholar]
- Liu, D.; Sun, J.; Huang, D.; Wang, X.; Cheng, K.; Yang, W.; Ding, J. Research on development status and technology trend of intelligent autonomous ammunition. In Journal of Physics: Conference Series; IOP Publishing: Bristol, UK, 2021; Volume 1721, p. 012032. [Google Scholar]
- MacGregor, D. Future Battle: The Merging Levels of War; Army War Coll Carlisle Barracks PA. 1992. Available online: https://apps.dtic.mil/sti/pdfs/ADA528099.pdf (accessed on 1 May 2021).
- Kiszely, J. Thinking about the operational level. RUSI J. 2005, 150, 38–43. [Google Scholar] [CrossRef]
- Božanić, D.; Ranđelović, A.; Radovanović, M.; Tešić, D. a hybrid lbwa-ir-mairca multi-criteria decision-making model for determination of constructive elements of weapons. Facta Univ. Ser. Mech. Eng. 2020, 18, 399–418. [Google Scholar]
- Dalkıran, E.; Önel, T.; Topçu, O.; Demir, K. Automated integration of real-time and non-real-time defense systems. Def. Technol. 2021, 17, 657–670. [Google Scholar] [CrossRef]
- Božanić, D.; Tešić, D.; Kočić, J. Multi-criteria FUCOM–Fuzzy MABAC model for the selection of location for construction of single-span bailey bridge. Decis. Mak. Appl. Manag. Eng. 2019, 2, 132–146. [Google Scholar] [CrossRef]
- Malbašić, S.; Đurić, S. Risk assessment framework: Application of Bayesian Belief Networks in an ammunition delaboration project. Vojnoteh. Glas. 2019, 67, 614–641. [Google Scholar] [CrossRef]
- Božanić, D.; Tešić, D.; Milić, A. Multicriteria decision making model with Z-numbers based on FUCOM and MABAC model. Decis. Mak. Appl. Manag. Eng. 2020, 3, 19–36. [Google Scholar] [CrossRef]
- Arkin, R. Lethal autonomous systems and the plight of the non-combatant. In The Political Economy of Robots; Palgrave Macmillan: Cham, Switzerland, 2018; pp. 317–326. [Google Scholar]
- Righetti, L.; Pham, Q.; Madhavan, R.; Chatila, R. Lethal autonomous weapon systems [ethical, legal, and societal issues]. IEEE Robot. Autom. Mag. 2018, 25, 123–126. [Google Scholar] [CrossRef]
- Hamurcu, M.; Eren, T. Selection of Unmanned Aerial Vehicles by Using Multicriteria Decision-Making for Defence. J. Math. 2020. [Google Scholar] [CrossRef]
- Kwon, H.; Yoon, H.; Choi, D. Restricted evasion attack: Generation of restricted-area adversarial example. IEEE Access 2019, 7, 60908–60919. [Google Scholar] [CrossRef]
- Kwon, H.; Yoon, H.; Park, K. Acoustic-decoy: Detection of adversarial examples through audio modification on speech recognition system. Neurocomputing 2020, 417, 357–370. [Google Scholar] [CrossRef]
Levels of Automation | Description |
---|---|
Level 1—Manual control. | The computer offers no assistance. |
Level 2—Decision proposal stage. | The computer offers some decision to the operator. The operator is responsible for deciding and executing. |
Level 3—Human decision select stage. | The human selects one decision, and the computer executes. |
Level 4—Computer decision stage. | The computer selects one decision and executes with human approval. |
Level 5—Computer execution and on human information stage. | The computer executes the selected decision and informs the human. |
Level 6—Computer execution and on-call human information stage. | The computer executes the selected decision and informs the human only if asked. |
Level 7—Computer execution and voluntary information stage. | The computer executes the selected decision and informs the human only if it decides to. |
Level 8—Autonomous control stage. | The computer does everything without human notification, except if an error that is not into the specifications occurs. In that case, the computer needs to inform the operator. |
Elsevier’s Scopus® Database | n |
---|---|
Identification | |
“Defense Industry” AND “Intelligent Systems” (All fields) | 153 |
Screening | |
Source type (Journals) | 67 |
Document type (Articles) | 61 |
Language (English) | 57 |
Eligibility | |
Full-text articles assessed for eligibility | 51 |
Included | |
Included studies (+11 articles) | 62 |
Modes of Autonomous Intelligent Systems | Description | Levels of War | Decisions Type | Types of Artificial Intelligence |
---|---|---|---|---|
Mode 1–Fully autonomous operation | The human has no control over the operation | Military tactical | Structured decision | Mechanical intelligence |
Mode 2–Partially autonomous operation | The human has some kind of control over the operation, or the system assists humans and vice-versa | Military operational | Semi-structured decisions | Thinking intelligence |
Mode 3– Smart autonomous decision-making | The intelligent system supports humans in case of need | Military strategic | Unstructured decisions | Feeling intelligence |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Reis, J.; Cohen, Y.; Melão, N.; Costa, J.; Jorge, D. High-Tech Defense Industries: Developing Autonomous Intelligent Systems. Appl. Sci. 2021, 11, 4920. https://doi.org/10.3390/app11114920
Reis J, Cohen Y, Melão N, Costa J, Jorge D. High-Tech Defense Industries: Developing Autonomous Intelligent Systems. Applied Sciences. 2021; 11(11):4920. https://doi.org/10.3390/app11114920
Chicago/Turabian StyleReis, João, Yuval Cohen, Nuno Melão, Joana Costa, and Diana Jorge. 2021. "High-Tech Defense Industries: Developing Autonomous Intelligent Systems" Applied Sciences 11, no. 11: 4920. https://doi.org/10.3390/app11114920