Phenotropic Interaction: Improving Interfaces with Computing with Words and Perceptions (Fuzzy Management Methods)

✍ Scribed by Moreno Colombo

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 178
• Category: Library

✦ Synopsis

Successful interaction between humans and artificial systems allows for combining the advantages of all actors in solving problems. However, interaction is often demanding for people, as it builds on artificial concepts, such as strict protocols.

This book presents the new paradigm of 'phenotropic' interaction, which aims to improve the naturalness of the interaction thanks to bio-inspired approaches. These include methods for understanding and reasoning with human perceptions expressed as natural language, fundamental to support the artificial system to better understand people's real desires and needs. Methods for improving the theories of computing with words and perceptions are developed in this book and applied to concrete use cases in prototypes enhancing the exchange of information with virtual assistants and smart city ecosystems. The presented use cases serve not only as examples of the application of the phenotropic interaction principles but also to verify their effective impact on communication.

✦ Table of Contents

Foreword
Preface
Acknowledgments
Contents
Acronyms
List of Figures
List of Tables
List of Algorithms
Part I Motivation and Objectives
1 Introduction
1.1 Background and Motivation
1.2 Research Objectives
1.3 Methodology
1.3.1 Design Science Research
1.3.2 Toward Antidisciplinary Research
1.4 Outline
1.5 Own Research Contribution
References
Part II Theory of Naturalness
2 Phenotropic Interaction
2.1 Phenotropics
2.2 Design Principles of Phenotropic Interaction
2.3 Phenotropic Interaction Framework
2.4 Concluding Remarks
References
3 Cognitive and Perceptual Computing
3.1 Conversation Theory
3.2 Computing with Words and Perceptions
3.3 Automated Reasoning
3.4 Interactive Machine Learning
3.5 Explainable Artificial Intelligence
3.6 Concluding Remarks
References
Part III Natural Language Conversations
4 Semantic Similarity Measures
4.1 Conceptual Similarity Measures
4.2 Spectral Similarity Measures
4.3 A Novel Spectral Similarity Measure
4.3.1 Choice of Knowledge Base
4.3.2 Spectral Semantic Similarity Measure
4.4 Practical Implementation Challenges
4.4.1 Thesaurus Selection
4.4.2 Multiple Meanings
4.4.3 Order of Synonymy
4.5 Evaluation
4.5.1 Methodology
4.5.2 Results
4.6 Concluding Remarks
References
5 Automatic Precisiation of Meaning
5.1 Automatic Precisiation of Meaning 1.0
5.1.1 Basis Selection and Precisiation
5.1.2 The APM 1.0 Algorithm
5.2 Automatic Precisiation of Meaning 2.0
5.2.1 Algorithm Choices
5.2.2 The APM 2.0 Algorithm
5.3 Evaluation
5.3.1 Methodology
5.3.2 Results
5.4 Concluding Remarks
References
6 Fuzzy Analogical Reasoning
6.1 Analogical Reasoning
6.2 The FAR Prototype
6.2.1 Conceptual Analogies
6.2.2 Spectral Analogies
6.2.3 Prototype Interface
6.3 Evaluation
6.3.1 Methodology
6.3.2 Results
6.4 Concluding Remarks
References
Part IV Applications of Phenotropic Interaction
7 Phenotropic Interaction in Virtual Assistants
7.1 Extension of If This Then That Rules
7.1.1 Query Preprocessing
7.1.2 Query Matching
7.1.3 Rule Adaptation with Fuzzy Analogical Reasoning
7.2 The FVA Prototype
7.3 Evaluation
7.3.1 Methodology
7.3.2 Results
7.4 Concluding Remarks
References
8 Phenotropic Interaction in Smart Cities
8.1 Jingle Jungle Maps
8.1.1 Problem Statement
8.1.2 Architecture
8.1.3 Evaluation
8.1.4 Phenotropic Interaction
8.2 Streetwise
8.2.1 Problem Statement
8.2.2 Architecture
8.2.3 Evaluation
8.2.4 Phenotropic Interaction
8.3 Concluding Remarks
References
Part V Conclusions
9 Outlook and Conclusions
9.1 Summary
9.2 Alignment with Research Questions
9.3 Future Developments
9.4 Outlook and Conclusions
Reference
Glossary
A Survey: Ordering of Scalar Terms
B Semantic Similarity Evaluation Details
C Evaluation of the FVA Prototype