株式会社極東書店トップ ＞ 商品一覧 ＞ Engineering Artificially Intelligent Systems: A Systems Engineering Approach to Realizing Synergistic Capabilities. 1st ed. 2021
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Engineering Artificially Intelligent Systems: A Systems Engineering Approach to Realizing Synergistic Capabilities. 1st ed. 2021
・ISBN 978-3-030-89384-2 paper EUR 64.99
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| 著者・編者 | Lawless, William F. / Llinas, James / Sofge, Donald A. / Mittu, Ranjeev (eds.), |
|---|---|
| シリーズ | (Lecture Notes in Computer Science) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2021 |
| ページ数 | 281 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A01-76911＞ |
解説
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.
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.