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AI and Complex Adaptive Systems: A L...

McGowan, Douglas.

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AI and Complex Adaptive Systems: A Lifecycle Methodology for Verification and Monitoring.

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI and Complex Adaptive Systems: A Lifecycle Methodology for Verification and Monitoring./
Author:	McGowan, Douglas.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	108 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: A.
Contained By:	Dissertations Abstracts International85-10A.
Subject:	Engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31145931
ISBN:	9798382211886

AI and Complex Adaptive Systems: A Lifecycle Methodology for Verification and Monitoring.
McGowan, Douglas.

AI and Complex Adaptive Systems: A Lifecycle Methodology for Verification and Monitoring. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 108 p.

Source: Dissertations Abstracts International, Volume: 85-10, Section: A.

Thesis (D.Engr.)--The George Washington University, 2024.

This praxis paper introduces a novel lifecycle methodology for the verification and monitoring of AI and Complex Adaptive Systems (CAS), blending traditional quality control mechanisms with dynamic approaches suited for AI's adaptive nature. Central to this methodology is the integration of Autonomic System concepts with established quality control practices, including Statistical Process Control (SPC), to manage the unpredictability and variability inherent in AI systems. The paper evaluates the methodology's effectiveness, highlighting its adaptability and efficiency in ensuring AI system reliability and performance.Insights into AI behavior, predictability, and the impact of continuous learning processes are discussed, underscoring the methodology's significance in the field of Systems Engineering. The paper concludes with recommendations for further improvements and future research directions, emphasizing the need for standardized protocols, ethical considerations, and the exploration of advanced AI architectures and hybrid systems. This research contributes to advancing Systems Engineering practices, particularly in the management of complex, evolving AI systems, and addresses the critical need for structured yet adaptable verification and monitoring approaches in the era of intelligent technologies.

ISBN: 9798382211886Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Complex Adaptive Systems

AI and Complex Adaptive Systems: A Lifecycle Methodology for Verification and Monitoring.
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245 1 0 $a AI and Complex Adaptive Systems: A Lifecycle Methodology for Verification and Monitoring.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 108 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-10, Section: A.
500 $a Advisor: Sarkani, Shahram;Mazzuchi, Thomas A.
502 $a Thesis (D.Engr.)--The George Washington University, 2024.
520 $a This praxis paper introduces a novel lifecycle methodology for the verification and monitoring of AI and Complex Adaptive Systems (CAS), blending traditional quality control mechanisms with dynamic approaches suited for AI's adaptive nature. Central to this methodology is the integration of Autonomic System concepts with established quality control practices, including Statistical Process Control (SPC), to manage the unpredictability and variability inherent in AI systems. The paper evaluates the methodology's effectiveness, highlighting its adaptability and efficiency in ensuring AI system reliability and performance.Insights into AI behavior, predictability, and the impact of continuous learning processes are discussed, underscoring the methodology's significance in the field of Systems Engineering. The paper concludes with recommendations for further improvements and future research directions, emphasizing the need for standardized protocols, ethical considerations, and the exploration of advanced AI architectures and hybrid systems. This research contributes to advancing Systems Engineering practices, particularly in the management of complex, evolving AI systems, and addresses the critical need for structured yet adaptable verification and monitoring approaches in the era of intelligent technologies.
590 $a School code: 0075.
650 4 $a Engineering. $3 586835
650 4 $a Computer science. $3 523869
650 4 $a Systems science. $3 3168411
650 4 $a Information science. $3 554358
653 $a Complex Adaptive Systems
653 $a Continuous monitoring
653 $a Machine learning
653 $a Quality control
653 $a Verification and Validation
690 $a 0537
690 $a 0984
690 $a 0723
690 $a 0800
690 $a 0790
710 2 $a The George Washington University. $b Engineering Management. $3 1262973
773 0 $t Dissertations Abstracts International $g 85-10A.
790 $a 0075
791 $a D.Engr.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31145931