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Explainable AI and Other Applications of Fuzzy Techniques : Proceedings of the 2021 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021 / edited by Julia Rayz, Victor Raskin, Scott Dick, Vladik Kreinovich

Material type: materialTypeLabelE-bookSeries: (Lecture Notes in Networks and Systems, 2367-3389 ; 258).Publisher: Cham : Springer International Publishing, 2022Edition: 1st edition 2022.Description: 1 recurso en línea (XII, 506 páginas) : 198 ilustraciones, 150 ilustraciones a color.ISBN: 9783030820992.Subject: Inteligencia artificial -- Congresos y asambleas | Lógica difusa -- Congresos y asambleasOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources Summary: This book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques. This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI-in short, to anyone who is interested in problems involving fuzziness and AI in general. .
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Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería Q334 2022 EB (Browse shelf(Opens below)) Acceso electrónico eBook.09012878
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This book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques. This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI-in short, to anyone who is interested in problems involving fuzziness and AI in general. .

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