國立虎尾科技大學 |

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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	AI and Complex Adaptive Systems :/
Reminder of title:	A Lifecycle Methodology for Verification and Monitoring.
Author:	McGowan, Douglas.
Description:	1 online resource (108 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: A.
Contained By:	Dissertations Abstracts International85-10A.
Subject:	Engineering. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (108 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382211886Subjects--Topical Terms:

561152
Engineering.
Subjects--Index Terms:

Complex Adaptive SystemsIndex Terms--Genre/Form:

554714
Electronic books.

AI and Complex Adaptive Systems : = A Lifecycle Methodology for Verification and Monitoring.
LDR:02813ntm a22004217 4500 001 1147868
005 20240916075418.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798382211886
035 $a (MiAaPQ)AAI31145931
035 $a AAI31145931
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a McGowan, Douglas. $3 1473700
245 1 0 $a AI and Complex Adaptive Systems : $b A Lifecycle Methodology for Verification and Monitoring.
264 0 $c 2024
300 $a 1 online resource (108 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Computer science. $3 573171
650 4 $a Systems science. $3 1148479
650 4 $a Information science. $3 561178
653 $a Complex Adaptive Systems
653 $a Continuous monitoring
653 $a Machine learning
653 $a Quality control
653 $a Verification and Validation
655 7 $a Electronic books. $2 local $3 554714
690 $a 0537
690 $a 0984
690 $a 0723
690 $a 0800
690 $a 0790
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The George Washington University. $b Engineering Management. $3 1179162
773 0 $t Dissertations Abstracts International $g 85-10A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31145931 $z click for full text (PQDT)