Multimodal Expressions of Social Attitude

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Table of contents

1 Introduction 
1.1 Context and Research Issues
1.2 Contributions
1.3 Manuscript Organization
1.4 Publications
2 Theoretical Background 
2.1 Attitude Definition
2.2 Attitude Representation
2.3 Interpersonal Circumplex Measurements and Interpretation
2.3.1 Measurements
2.3.2 Scoring and Interpreting the IPC Measurements
2.4 Multimodal Expressions of Social Attitude
2.5 Non-verbal Behavior Interpretation
3 State of the Art on Attitude Modeling for Virtual Agents 
3.1 Attitude Modeling for Embodied Conversational Agents
3.1.1 ECA’s Behavior Expressing Attitude
3.1.2 Attitude Dynamics over Time
3.1.3 Attitude Generation Models
3.2 Sequence-Based Multimodal Behavior Modeling
4 Sequence Mining: State of the Art and Our Algorithm 
4.1 Non-Temporal Algorithms
4.2 Temporal Algorithms
4.3 Temporal Sequence Mining Algorithms
4.4 HCApriori Algorithm
4.4.1 Definitions
4.4.2 Algorithm
4.4.3 Evaluation and Results
4.5 Pattern Quality Assessment
4.6 Conclusion
5 Sequence-Based Attitude Variation Modeling 
5.1 Extraction of Relevant Patterns Expressing Attitude Variations
5.1.1 Building Sequence Databases Representing Attitude Variations
5.1.2 Pattern Extraction
5.2 Evaluation of the Extracted Patterns
5.2.1 Experimental Design
5.2.2 Measures
5.2.3 Hypotheses
5.2.4 Results
5.2.5 Discussion
5.3 Conclusion
6 Attitude Planner 
6.1 GRETA-VIB
6.2 Sequential Attitude Planner Model
6.2.1 Intention Sequence Generation
6.2.2 Attitude Sequence Selection
6.2.3 Intention Sequence Enrichment
6.2.4 Signal Replacement
6.3 Evaluation
6.3.1 Experimental Design
6.3.2 Results
6.4 Discussion
6.5 Conclusion
7 Generative Model of Agent’s Behaviors in Human-Agent Interaction 
7.1 Related works
7.2 Corpus
7.3 Neural Networks and LSTM
7.3.1 Neural Networks: Overview
7.3.2 Recurrent Neural Networks: LSTM
7.4 LSTM Model
7.4.1 Prediction Model
7.4.2 Evaluation
7.5 Architecture
7.5.1 EyesWeb: User’s Behavior Analysis
7.5.2 Flipper: Dialog Management
7.5.3 GRETA-VIB
7.5.4 BehaviorPrediction: Agent’s Behavior Prediction
7.6 Evaluation
7.6.1 Independent Variables
7.6.2 Measures
7.6.3 Hypotheses
7.6.4 Protocol
7.7 Results
7.8 Conclusion
8 Engagement Modeling in Dyadic Interactions 
8.1 Related Works
8.1.1 Engagement-Related Behaviors
8.1.2 Engagement Prediction
8.2 LSTM Model for Engagement Prediction
8.2.1 Data
8.2.2 Model
8.2.3 Results
9 Conclusion 
9.1 Summary
9.1.1 Attitude Variation Modeling
9.1.2 Adapting Agent’s Behavior According to the User’s Behavior
9.1.3 Engagement Prediction
9.2 Limits and Perspectives
Appendices 
A Results of the First Study 
B Results of the Second Study 
C Engagement Prediction