Explainable AI and other applications of fuzzy techniques : proceedings of the 2021 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021 / Julia Rayz, Victor Raskin, Scott Dick, Vladik Kreinovich, editor.
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...
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Full Text (via Springer) |
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| Other Authors: | , , , |
| Other title: | NAFIPS 2021. |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer,
[2022]
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| Series: | Lecture notes in networks and systems ;
v. 258. |
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Table of Contents:
- Intro
- Explainability: New Application and New Promise of Fuzzy Techniques
- Contents
- A Fuzzy Logic Approach for Spacecraft Landing Site Selection
- 1 Introduction
- 2 Image Dataset
- 3 Methodology
- 3.1 Image Size Reduction
- 3.2 Object Grouping
- 3.3 Object Classification
- 3.4 Performance Metrics
- 4 Results
- 5 Conclusions and Future Work
- 5.1 Future Work
- References
- Takagi-Sugeno Fuzzy Systems with Triangular Membership Functions as Interpretable Neural Networks
- 1 Introduction
- 2 Preliminaries
- 2.1 Fuzzy Systems
- 2.2 Neural Networks.
- 3 Equivalence Between TS Fuzzy Systems with Triangular Membership And Neural Networks with ReLU Activation
- 3.1 TS Fuzzy Systems Expressed in Terms of ReLU Functions
- 3.2 TS Fuzzy Systems as Neural Networks with ReLU Activation
- 3.3 Neural Networks Expressed in Terms of Takagi-Sugeno Fuzzy Systems
- 4 Conclusions
- References
- A Study on Constrained Interval Arithmetic
- 1 Introduction
- 2 Constrained Interval Arithmetic
- 2.1 Addition in CIA
- 2.2 Difference in CIA
- 2.3 Multiplication in CIA
- 2.4 Division in CIA
- 2.5 Additive/Multiplicative Inverse in CIA.
- 3 Distributivity, Expression/component-Wise and Optimization
- 4 Inclusion Isotonicity
- 5 Final Remarks
- References
- Fuzzy Classification of Multi-intent Utterances
- 1 Introduction
- 2 Related Work
- 3 Utterance Level Fuzzy Memberships
- 3.1 Membership Functions
- 3.2 Parameter Generation
- 4 Single Intent to Multi Intent Utterances
- 5 Fuzzy Membership Aggregation and Defuzzification
- 6 Experiments
- 6.1 Setup
- 6.2 Data
- 6.3 Training Details
- 6.4 Results
- 7 Conclusion
- References
- How Much for a Set: General Case of Decision Making Under Set-Valued Uncertainty.
- 1 Decision Making Under Set-Valued Uncertainty: Formulation of the Problem
- 2 What Is Known: Cases of Closed Intervals and Closed Sets
- 3 Extending the Known Result to General Bounded Sets
- 4 What if We Do Not Require Additivity?
- Reference
- A Deep Fuzzy Semi-supervised Approach to Clustering and Fault Diagnosis of Partially Labeled Semiconductor Manufacturing Data
- 1 Introduction
- 2 Methodology
- 2.1 Deep Convolutional Unsupervised Feature Learning
- 2.2 PCA-Based Semi-supervised Fault Classification
- 2.3 Fuzzy c-means Clustering and Borderline Case Detection
- 3 Results.
- 3.1 Case Study Description
- 3.2 Semi-supervised Classification Results
- 3.3 Fuzzy c-means Clustering Results
- 4 Discussion
- 5 Conclusion
- References
- Why Fuzzy Techniques in Explainable AI? Which Fuzzy Techniques in Explainable AI?
- 1 Why Fuzzy Techniques in Explainable AI
- 2 Which Fuzzy Techniques in Explainable AI
- References
- Why Are Fuzzy and Stochastic Calculus Different?
- 1 Introduction
- 2 Introduction to Stochastic Equations and First Issues with Derivatives
- 3 Modes of Convergence
- 4 Convergence in Distribution
- 5 Is Weakening the Topology a Viable Solution?