Explainable AI and Other Applications of Fuzzy Techniques

Julia Rayz

ISBN 10: 303082098X / ISBN 13: 9783030820985

Published by Springer, Palgrave Macmillan Jul 2021, 2021

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 520 pp. Englisch.

Seller Inventory # 9783030820985

Bibliographic Details

• Title: Explainable AI and Other Applications of Fuzzy Techniques
• Publisher: Springer, Palgrave Macmillan Jul 2021
• Publication Date: 2021
• Language: English
• ISBN 10: 303082098X
• ISBN 13: 9783030820985
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 28D millimeters 235 millimeters width by 155 millimeters height by 28 millimeters depth
• Weight: 779 grams

About this title

Synopsis

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.