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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

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Julia Rayz

    1. Purdue University, West Lafayette, USA

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  2. Victor Raskin

    1. Purdue University, West Lafayette, USA

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  3. Scott Dick

    1. University of Alberta, Edmonton, Canada

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  4. Vladik Kreinovich

    1. University of Texas at El Paso, El Paso, USA

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  • Is of interest to practitioners, researchers, graduate students, and anyone interested in problem-solving fuzziness
  • Presents many artificial intelligence (AI) techniques that do not explain their recommendations
  • Provides natural language explanations for numerical AI recommendations

Part of the book series: Lecture Notes in Networks and Systems (LNNS, volume 258)

Included in the following conference series:

Conference proceedings info: NAFIPS 2021.

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Table of contents (45 papers)

  1. Front Matter

    Pages i-xii
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  2. A Fuzzy Logic Approach for Spacecraft Landing Site Selection

    • Andrew Barth, Kelly Cohen, Ou Ma
    Pages 1-13

  3. Takagi-Sugeno Fuzzy Systems with Triangular Membership Functions as Interpretable Neural Networks

    • Barnabas Bede, Aeon Williams
    Pages 14-25

  4. A Study on Constrained Interval Arithmetic

    • Flaulles Boone Bergamaschi, Regivan Hugo Nunes Santiago
    Pages 26-36

  5. Fuzzy Classification of Multi-intent Utterances

    • Geetanjali Bihani, Julia Taylor Rayz
    Pages 37-51

  6. How Much for a Set: General Case of Decision Making Under Set-Valued Uncertainty

    • Laxman Bokati, Olga Kosheleva, Vladik Kreinovich
    Pages 52-61

  7. A Deep Fuzzy Semi-supervised Approach to Clustering and Fault Diagnosis of Partially Labeled Semiconductor Manufacturing Data

    • Joseph Cohen, Jun Ni
    Pages 62-73

  8. Why Fuzzy Techniques in Explainable AI? Which Fuzzy Techniques in Explainable AI?

    • Kelly Cohen, Laxman Bokati, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich
    Pages 74-78

  9. Why Are Fuzzy and Stochastic Calculus Different?

    • Gustavo do Nascimento Costa, Laecio Carvalho de Barros
    Pages 79-91

  10. A Genetic Fuzzy Approach for the Prediction of Heart Failure

    • Nicholas DeGroote, Kelly Cohen
    Pages 92-100

  11. COVID-19 Vaccination Priority Evaluation

    • Jozo Dujmović, Daniel Tomasevich
    Pages 101-115

  12. Comparing the Explainability and Performance of Reinforcement Learning and Genetic Fuzzy Systems for Safe Satellite Docking

    • Kyle Dunlap, Kelly Cohen, Kerianne Hobbs
    Pages 116-129

  13. A Note on the KM Algorithm for Computing the Variance of an Interval Type-2 Fuzzy Set

    • Juan Carlos Figueroa-García, Carlos Franco, Jhoan Sebastian Tenjo-García
    Pages 130-140

  14. Estimating Fuzzy Possibility Functions for E-Commerce Decision Support

    • Camilo Franco, Daniela Cadavid
    Pages 141-153

  15. Machine Learning to Augment the Fusion Process for Data Classification

    • Michael J. Hirsch, James A. Crowder
    Pages 154-165

  16. Managing Uncertainty in Crowdsourcing with Interval-Valued Labels

    • Chenyi Hu, Victor S. Sheng, Ningning Wu, Xintao Wu
    Pages 166-178

  17. Modeling Fuzzy Cluster Transitions for Topic Tracing

    • Xiaonan Jing, Yi Zhang, Qingyuan Hu, Julia Taylor Rayz
    Pages 179-189

  18. A Natural Formalization of Changing-One’s-Mind Leads to Square Root of “Not” and to Complex-Valued Fuzzy Logic

    • Olga Kosheleva, Vladik Kreinovich
    Pages 190-195

  19. Each Realistic Continuous Functional Dependence Implies a Relation Between Some Variables: A Theoretical Explanation of a Fuzzy-Related Empirical Phenomenon

    • Olga Kosheleva, Vladik Kreinovich
    Pages 196-202

  20. Uniform Mixture Convergence of Continuously Transformed Fuzzy Systems

    • Bart Kosko
    Pages 203-216
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Other volumes

  1. Explainable AI and Other Applications of Fuzzy Techniques

Keywords

About this book

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.


Editors and Affiliations

  • Purdue University, West Lafayette, USA

    Julia Rayz, Victor Raskin

  • University of Alberta, Edmonton, Canada

    Scott Dick

  • University of Texas at El Paso, El Paso, USA

    Vladik Kreinovich

Bibliographic Information

  • Book Title: Explainable AI and Other Applications of Fuzzy Techniques

  • Book Subtitle: Proceedings of the 2021 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021

  • Editors: Julia Rayz, Victor Raskin, Scott Dick, Vladik Kreinovich

  • Series Title: Lecture Notes in Networks and Systems

  • DOI: https://doi.org/10.1007/978-3-030-82099-2

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Softcover ISBN: 978-3-030-82098-5Published: 28 July 2021

  • eBook ISBN: 978-3-030-82099-2Published: 27 July 2021

  • Series ISSN: 2367-3370

  • Series E-ISSN: 2367-3389

  • Edition Number: 1

  • Number of Pages: XII, 506

  • Number of Illustrations: 48 b/w illustrations, 150 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence, Data Engineering

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