Mittu ranjeev (205 Ergebnisse)

XII, 270 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. pp. 192, Maps.

Zustand: New. pp. 192 Illus., Maps.

Zustand: New. pp. 192.

Paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Zustand: As New. Unread book in perfect condition.

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Paperback. Zustand: New. 1st ed. 2021. 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.…

Zustand: As New. Unread book in perfect condition.

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Zustand: As New. Unread book in perfect condition.

Zustand: New. In English.

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Zustand: New. 1st edition NO-PA16APR2015-KAP.

Zustand: New.

Zustand: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

Zustand: New.

Hardcover. Zustand: Very Good. 1. Auflage. unread, some shelfwear - will be dispatched immediately.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Many current AI and machine learning algorithms and data and information fusion processesattempt in software to estimate situations in our complex world of nested feedback loops. Suchalgorithms and processes must gracefully and efficiently adapt to technical challenges such asdata quality induced by these loops, and interdependencies that vary in complexity, space, andtime.To realize effective and efficient designs of computational systems, a Systems Engineeringperspective may provide a framework for identifying the interrelationships and patterns ofchange between components rather than static snapshots. We must study cascadinginterdependencies through this perspective to understand their behavior and to successfullyadopt complex system-of-systems in society.This book derives in part from the presentations given at the AAAI 2021 Spring Symposiumsession on Leveraging Systems Engineering to Realize Synergistic AI / Machine LearningCapabilities. Its 16 chapters offer an emphasis on pragmatic aspects and address topics insystems engineering; AI, machine learning, and reasoning; data and information fusion;intelligent systems; autonomous systems; interdependence and teamwork; human-computerinteraction; trust; and resilience.…

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 284 | Sprache: Englisch | Produktart: Bücher | This volume explores the intersection of robust intelligence (RI) and trust in autonomous systems across multiple contexts among autonomous hybrid systems, where hybrids are arbitrary combinations of humans, machines and robots. To better understand the relationships between artificial intelligence (AI) and RI in a way that promotes trust between autonomous systems and human users, this book explores the underlying theory, mathematics, computational models, and field applications. It uniquely unifies the fields of RI and trust and frames it in a broader context, namely the effective integration of human-autonomous systems. A description of the current state of the art in RI and trust introduces the research work in this area. With this foundation, the chapters further elaborate on key research areas and gaps that are at the heart of effective human-systems integration, including workload management, human computer interfaces, team integration and performance, advanced analytics, behavior modeling, training, and, lastly, test and evaluation. Written by international leading researchers from across the field of autonomous systems research, Robust Intelligence and Trust in Autonomous Systems dedicates itself to thoroughly examining the challenges and trends of systems that exhibit RI, the fundamental implications of RI in developing trusted relationships with present and future autonomous systems, and the effective human systems integration that must result for trust to be sustained. Contributing authors: David W. Aha, Jenny Burke, Joseph Coyne, M.L. Cummings, Munjal Desai, Michael Drinkwater, Jill L. Drury, Michael W. Floyd, Fei Gao, Vladimir Gontar, Ayanna M. Howard, Mo Jamshidi, W.F. Lawless, Kapil Madathil, Ranjeev Mittu, Arezou Moussavi, Gari Palmer, Paul Robinette, Behzad Sadrfaridpour, Hamed Saeidi, Kristin E. Schaefer, Anne Selwyn, Ciara Sibley, Donald A. Sofge, Erin Solovey, Aaron Steinfeld, Barney Tannahill, Gavin Taylor, Alan R. Wagner, Yue Wang, Holly A. Yanco, Dan Zwillinger.…

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Paperback. Zustand: Brand New. 328 pages. 9.25x6.14x0.94 inches. In Stock.

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Zustand: As New. Unread book in perfect condition.

hardcover. Zustand: Very Good.