American Journal of Artificial Intelligence
Volume 4, Issue 1, June 2020, Pages: 30-35
Received: Mar. 14, 2020;
Accepted: Mar. 25, 2020;
Published: Apr. 30, 2020
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Kalesanwo Olamide, School of Computing and Engineering Sciences, Babcock University, Ilishan Remo, Nigeria
Kuyoro ‘Shade, School of Computing and Engineering Sciences, Babcock University, Ilishan Remo, Nigeria
Eze Monday, School of Computing and Engineering Sciences, Babcock University, Ilishan Remo, Nigeria
Awodele Oludele, School of Computing and Engineering Sciences, Babcock University, Ilishan Remo, Nigeria
The advancement of technology has heralded novel computing devices and gadgets like self-driving cars, IoT devices, and autonomous systems. These advancements required high computational demand in achieving its goals. In matching the high computational demand of these new technologies, machine learning, parallelism, multicore processing and scaling are some of the approaches and techniques put in place. However, there is a pressure on the architectural development of recent computing devices as the traditional transistors seem to be fast outgrown. This article examines the reliability of autonomous systems using the PRISMA approach. Autonomous systems are systems that can fully operate and perform operations (computational or otherwise) with minimal human intervention. They are also capable of evaluating their performance. Thus, there is a need for a high degree of reliability. Several existing autonomous systems were reviewed and reliability issues of these systems were discussed. It was discovered that the reliability of a complex system is dependent on the reliability of underlying individual components and compromise of any of the underlying components of the autonomous system can affect the overall reliability of the entire system. The effort to enhance the reliability of these components will, in turn, improve the reliability of the entire system.
Autonomous Systems and Reliability Assessment: A Systematic Review, American Journal of Artificial Intelligence.
Vol. 4, No. 1,
2020, pp. 30-35.
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