Engineering Artificially Intelligent Systems: A Systems Engineering Approach to Realizing Synergistic Capabilities: 13000 (Lecture Notes in Computer Science, 13000)

William F. Lawless,James Llinas,Donald A. Sofge,Ranjeev Mittu

ISBN 10: 3030893847 / ISBN 13: 9783030893842

Published by Springer 2021-11-17, 2021

About this Item

Seller Inventory # 6666-IUK-9783030893842

Bibliographic Details

• Title: Engineering Artificially Intelligent Systems: A Systems Engineering Approach to Realizing Synergistic Capabilities: 13000 (Lecture Notes in Computer Science, 13000)
• Publisher: Springer 2021-11-17
• Publication Date: 2021
• Language: English
• ISBN 10: 3030893847
• ISBN 13: 9783030893842
• Binding: Paperback
• Condition: New

About this title

Synopsis

Many current AI and machine learning algorithms and data and information fusion processes attempt in software to estimate situations in our complex world of nested feedback loops. Such algorithms and processes must gracefully and efficiently adapt to technical challenges such as 

data quality induced by these loops, and interdependencies that vary in complexity, space, and time.

To realize effective and efficient designs of computational systems, a Systems Engineering perspective may provide a framework for identifying the interrelationships and patterns of change between components rather than static snapshots. We must study cascading interdependencies through this perspective to understand their behavior and to successfully adopt complex system-of-systems in society. 

This book derives in part from the presentations given at the AAAI 2021 Spring Symposium session on Leveraging Systems Engineering to Realize Synergistic AI / Machine Learning Capabilities. Its 16 chapters offer an emphasis on pragmatic aspects and address topics in systems engineering; AI, machine learning, and reasoning; data and information fusion; intelligent systems; autonomous systems; interdependence and teamwork; human-computer interaction; trust; and resilience.

From the Back Cover

Many current AI and machine learning algorithms and data and information fusion processes attempt in software to estimate situations in our complex world of nested feedback loops. Such algorithms and processes must gracefully and efficiently adapt to technical challenges such as 

data quality induced by these loops, and interdependencies that vary in complexity, space, and time.

To realize effective and efficient designs of computational systems, a Systems Engineering perspective may provide a framework for identifying the interrelationships and patterns of change between components rather than static snapshots. We must study cascading interdependencies through this perspective to understand their behavior and to successfully adopt complex system-of-systems in society. 

This book derives in part from the presentations given at the AAAI 2021 Spring Symposium session on Leveraging Systems Engineering to Realize Synergistic AI / Machine Learning Capabilities. Its 16 chapters offer an emphasis on pragmatic aspects and address topics in systems engineering; AI, machine learning, and reasoning; data and information fusion; intelligent systems; autonomous systems; interdependence and teamwork; human-computer interaction; trust; and resilience.

The chapter “How Interdependence Explains the World of Team work” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.