Explainable Uncertain Rule Based Fuzzy Systems
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Explainable Uncertain Rule-Based Fuzzy Systems
The third edition of this textbook presents a further updated approach to fuzzy sets and systems that can model uncertainty -- i.e., "type-2" fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications, from time-series forecasting to knowledge mining to classification to control and to explainable AI (XAI). This latest edition again begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty, leading to type-2 fuzzy sets and systems. New material is included about how to obtain fuzzy set word models that are needed for XAI, similarity of fuzzy sets, a quantitative methodology that lets one explain in a simple way why the different kinds of fuzzy systems have the potential for performance improvements over each other, and new parameterizations of membership functions that have the potential for achieving even greater performance for all kinds of fuzzy systems. For hands-on experience, the book provides information on accessing MATLAB, Java, and Python software to complement the content. The book features a full suite of classroom material.
Explainable AI and Other Applications of Fuzzy Techniques
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.
Information Processing and Management of Uncertainty in Knowledge-Based Systems
Author: Marie-Jeanne Lesot
language: en
Publisher: Springer Nature
Release Date: 2025-01-04
This book is a collection of papers focused on techniques for managing uncertainty and aggregation. It provides a forum for exchanging ideas between theoreticians and practitioners in these and related areas. The papers are part of the 20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, which will occur in Lisbon, Portugal, from July 22 to 26, 2024. The collection describes the latest findings on topics such as advances in fuzzy systems and data analysis, optimization, scheduling via modeling uncertainty, explainability, decision-making, implications, data aggregation, and aggregation operators. A special chapter is dedicated to the memory of Michio Sugeno. The book is a valuable resource for practitioners, researchers, and graduate students who want to apply fuzzy-based techniques to real-world data analysis and management processes involving imprecision and uncertainty.