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

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.

Author

• McGowan, Douglas

Language

• English

Keyword

• Machine Learning
• Verification and Validation
• Continuosu Monitoring
• Artificial Intelligence
• Complex Adaptive Systems
• Quality Control

Date created

• 2024

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Mazzuchi, Thomas A
• Sarkani, Shahram

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/5712m7598