Explainable Ambient Intelligence (XAmI): Explainable Artificial Intelligence Applications in Smart Life (SpringerBriefs in Applied Sciences and Technology)

✍ Scribed by Tin-Chih Toly Chen

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 113
• Category: Library

✦ Synopsis

This book systematically reviews the progress of Explainable Ambient Intelligence (XAmI) and introduces its methods, tools, and applications.

Ambient intelligence (AmI) is a vision in which an environment supports the people inhabiting it in an unobtrusive, interconnected, adaptable, dynamic, embedded, and intelligent way. So far, artificial intelligence (AI) technologies have been widely applied in AmI. However, some advanced AI methods are not easy to understand or communicate, especially for users with insufficient background knowledge of AI, which undoubtedly limits the practicability of these methods. To address this issue, explainable AI (XAI) has been considered a viable strategy. Although XAI technologies and tools applied in other fields can also be applied to explain AI technology applications in AmI, users should be the main body in the application of AmI, which is slightly different from the application of AI technologies in other fields.

This book containsreal case studies of the application of XAml and is a valuable resource for students and researchers.

✦ Table of Contents

Contents
1 Ambient Intelligence (AmI)
1.1 Ambient Intelligence (AmI)
1.2 Architecture and Operational Procedure of AmI Systems
1.3 Examples of AmI Applications
1.4 Issues and Challenges of Existing AmI Applications
1.5 Artificial Intelligence (AI) for Enhancing the Effectiveness of AmI Applications
1.6 Explainable Ambient Intelligence (XAmI): Explainable Artificial Intelligence (XAI) Applications to AmI
1.7 Examples of XAmI Applications
1.8 Organization of This Book
References
2 Explainable Artificial Intelligence (XAI) with Applications
2.1 Explainable Artificial Intelligence (XAI)
2.2 Explainability of ML Models
2.3 XAI for Explaining and Enhancing AI Applications in Ambient Intelligence (AmI)
2.4 Requirements for Trustable AI and XAI
2.5 Classification of XAI Methods
2.6 XAI Applications in Various Domains
2.7 Types of Explanations
2.8 XAI Techniques for Feature Importance Evaluation
References
3 XAmI Applications to Smart Homes
3.1 Artificial Intelligence (AI) Applications in Smart Homes
3.2 XAmI Technique for Explaining Fuzzy Logic Applications in Smart Homes
3.3 XAmI Technique for Explaining ANN Applications in Smart Homes
3.3.1 Interpretable Model-Agnostic Explanation (LIME)
3.3.2 LIME + Decision Trees (DTs)
3.3.3 LIME + Random Forests (RFs)
3.3.4 LIME + Fuzzy Inference Rules
References
4 XAmI Applications to Location-Aware Services
4.1 Artificial Intelligence (AI) Applications in Location-Aware Services (LASs)
4.2 Issues of Existing AI Applications in LASs
4.3 XAmI Applications in Location-Aware Services
4.3.1 Suitable XAmI Methods for Location-Aware Services
4.3.2 Various Types of Explanations for Location-Aware Services
4.3.3 Visualization XAmI Methods for Location-Aware Services
4.4 Criteria Priority Versus Impact
4.5 Explaining AI-Based Optimization in LASs
4.5.1 Local and Contrastive Explanations for AI-Based Optimization in LASs
References
5 XAmI Applications to Telemedicine and Telecare
5.1 Artificial Intelligence (AI) Applications in Telemedicine and Telecare
5.2 Effectiveness of AI Applications for Telemedicine and Telecare Services
5.3 Explainable Ambient Intelligence (XAmI) Applications in Telemedicine and Telecare
5.4 Popular XAmI Techniques for Telemedicine and Telecare
5.4.1 SHAP
5.4.2 LIME with Decision Tree (DT) Application
5.4.3 LIME with Random Forest (RF) Application
5.5 Applicability Assessment of Telemedicine and Telecare Services Based on XAmI
5.6 Issues with Existing XAmI Applications in Telemedicine and Telecare
References