Conversational Informatics: A Data-Intensive Approach with Emphasis on Nonverbal Communication


Conversational Informatics: A Data-Intensive Approach with Emphasis on Nonverbal Communication (2014) .. by Toyoaki Nishida, etc


Contents

1 Artificial Intelligence and Conversational Intelligence
1.1 Conversation as a Focus of Interdisciplinary Study
1.2 Conversational Informatics in Info-plosion and Techno-plosion
1.3 Primordial Soup of Conversation
1.4 Organization of This Book
1.5 Summary

2 Conversation: Above and Beneath the Surface
2.1 The Horizon of Conversational Communication
2.2 Stories and Narratives
2.3 Conversation in a Social Discourse
2.4 Interactions in Focused Gatherings
2.5 Joint Activity Theory
2.6 Integrating Multiple Modalitis to Make Sense
2.7 Turn-Taking System 2.8 Cognitive Process2.9 Summary

3 History of Conversational System Development
3.1 A Bird’s Eye View
3.2 Early Natural Language Dialogue Systems
3.2.1 Baseball
3.2.2 LUNAR
3.2.3 SHRDLU
3.2.4 ELIZA
3.3 Speech Dialogue Systems and Multimodal Interfaces
3.4 Embodied Conversational Agents and Intelligent Virtual Humans
3.5 Story Understanding/Generation Systems
3.6 Cognitive Computing
3.7 Towards Synergy
3.8 Summary

4 Methodologies for Conversational System Development
4.1 Introduction
4.2 Architecture
4.3 Scripts and Markup Languages 4.4 Basing Behaviors on Conversation Corpus
4.5 Behavior Learning Using Motif Discovery
4.5.1 Motif Discovery in Discrete Sequences
4.5.2 Motif Discovery in Real-Valued Time-Series
4.5.3 Symbolization Approaches
4.5.4 Exact Motif Discovery Approaches
4.5.5 Constrained Motif Discovery Approaches
4.6 Evaluation
4.7 Summary

5 Conversation Quantization
5.1 Framework of Conversation Quantization
5.2 The Representation Scheme
5.3 The Production/Consumption Scheme
5.4 Manipulation Scheme
5.5 Circulation Scheme
5.6 Augmenting Conversation Through Conversation Quantization
5.6.1 Shared Virtual Meeting Space5.6.2 Virtual Interaction Game
5.6.3 Tele-presence
5.7 Historical Notes
5.8 Summary

6 Smart Conversation Space
6.1 The Architecture of Smart Conversation Space
6.2 Situated Knowledge Media
6.3 Capturing Human Behavior in Open Conversation Space
6.4 Immersive Collaborative Interaction Environment
6.4.1 Providing a First-Person Perspective
6.4.2 Obtaining Social Interaction Behavior
6.4.3 DEAL: A Platform for Constructing the ICIE
6.5 Application of the ICIE
6.5.1 Filming Agent
6.5.2 Cooperative Multi-agent Interaction
6.5.3 Tele-presence 6.6 Summary7 Computer Vision Techniques for Conversational Interaction

7 Computer Vision Techniques for Conversational Interaction
7.1 Human Emotional State Recognition Through Visual Recognition Technology
7.2 Face Detection Techniques
7.3 Recognition of Facial Expressions
7.4 Facial Parameterization
7.4.1 Facial Action Coding System
7.4.2 Face Animation Parameter
7.5 Facial Animation Synthesis
7.6 Gesture Recognition and Synthesis
7.7 Gesture Descriptor and Synthesis
7.7.1 Labanotation
7.7.2 Data-Driven Approach for Gesture Synthesis
7.8 Summary

8 Measurement, Analysis and Modeling
8.1 Methodological Issues in Multi-modal Interaction Analysis
8.1.1 Experimental Planning
8.1.2 Building Experimental Environment
8.1.3 Preliminary Experiment
8.1.4 Full-Scale Experiment
8.1.5 Data Analysis and Interpretation
8.1.6 Collaborative Annotation
8.1.7 Physiological Signal Analysis
8.2 Natural Interaction Measurement
8.3 Measuring Social Atmosphere
8.3.1 Methods for Obtaining the I-Measure
8.3.2 Experiment to Record I-Measure Responses
8.3.3 Analyses of the Effects of an Atmosphere
8.3.4 Discussion
8.4 Extracting Evaluation Criteria for Ballroom Dancing
8.4.1 Ballroom Dance Evaluation Support System
8.4.2 Evaluation Experiment
8.4.3 Results and Discussions
8.5 Summary

9 From Observation to Interaction
9.1 Imitation, simulation and conversation
9.1.1 What is Imitation?
9.1.2 Imitation in Infants, Children and Adults
9.1.3 Understanding Others: Simulation
9.1.4 The Road to Conversation
9.2 Imitation in Artificial Agents
9.3 Implications of simulation theory for Interaction Modeling
9.3.1 Simultaneous Role Learning
9.3.2 Hierarchical Interaction Layers
9.3.3 Cognitive Indistinguishability Between Roles
9.4 Interaction as Simulation: System Architecture
9.5 Simulation Based Interaction Learned Through Imitation
9.5.1 Interaction Babbling: Learning BIAs
9.5.2 Imitation’s Road to Interaction: Learning ICPs
9.6 Simulation Based Behavior Generation
9.7 Summary

10 Applications of Simulation and Imitation for Interaction Learning
10.1 Case Study: Learning Gaze Behavior
10.1.1 Reactive Gaze controller
10.1.2 SILI Controller
10.1.3 Interaction Dimensions
10.1.4 Training Data Collection
10.1.5 Learning Through Imitation
10.1.6 Simulation Based Interaction
10.2 Fluid Imitation: Imitation in Social Context
10.2.1 Self-Initiated Behavior
10.2.2 Object-Caused Behavior
10.2.3 Relevance-Informed Learning
10.3 Summary

11 Cognitive Design for Discussion Support
11.1 Cognitive Framework for Cognitive Support
11.2 Analysis of Facilitating Behavior
11.2.1 Data Collection
11.2.2 Data Analysis
11.2.3 Insights Obtained
11.3 Dynamic Estimation of Emphasizing Points
11.3.1 Dynamically Estimating Emphasizing Points
11.3.2 Evaluation of DEEP
11.4 Dynamically Estimating Emphasizing Points for Group Decision-Making
11.4.1 Dynamic Estimation of Emphasizing Points Extended to Group Decision Making (gDEEP)
11.4.2 Evaluation Experiment
11.5 Facilitative Agent
11.5.1 A Facilitative Decision-Making Support Agent
11.5.2 Experiment
11.5.3 Discussion
11.6 Summary

12 Discussions
12.1 Conversational Knowledge Circulation
12.2 Social Intelligence Design
12.2.1 The Fast Interaction Loop on the Microscopic Level
12.2.2 The Strcutured Interactions at the Mesoscopic Level
12.2.3 The Networked Interactions at the Macroscopic Level
12.3 Ethical Aspects
12.4 Empathy
12.5 Summary

13 Conclusion

References
Index

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