Paper Info
Title
Reinforcement Emotion-Cognition System: A Teaching Words Task
Abstract
The goal of this paper is to suggest a system for intelligent learning environments with robots modeling of emotion regulation and cognition based on quantitative motivation. A detailed interactive situation for teaching words is proposed. In this study, we introduce one bottom-up collaboration method for emotion-cognition interplay and behaviour decision-making. Integration with gross emotion regulation theory lets the proposed system adapt to natural interactions between students and the robot in emotional interaction. Four key ideas are advocated, and they jointly set up a reinforcement emotion-cognition system (RECS). First, the quantitative motivation is grounded on external interactive sensory detection, which is affected by memory and preference. Second, the emotion generation triggered by an initial motivation such as external stimulus is also influenced by the state in the previous time. Third, the competitive and cooperative relationship between emotion and motivation intervenes to make the decision of emotional expression and teaching actions. Finally, cognitive reappraisal, the emotion regulation strategy, is introduced for the establishment of emotion transition combined with personalized cognition. We display that this RECS increases the robot emotional interactive performance and makes corresponding teaching decision through behavioural and statistical analysis.
Year
DOI
Venue
2019
10.1155/2019/8904389
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
Field
DocType
Volume
Cognitive reappraisal,Computer science,Cognitive psychology,Emotional expression,Artificial intelligence,Stimulus (physiology),Sensory system,Cognition,Robot,Reinforcement,Machine learning,Statistical analysis
Journal
2019
ISSN
Citations 
PageRank 
1687-5265
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Minjia Li100.68
Lun Xie22710.06
Anqi Zhang300.34
Fuji Ren4803135.33