  Emotions, as a fundamental ingredient of any social interaction, lead to
behaviors that represent the effectiveness of the interaction through facial
expressions and gestures in humans. Hence an agent must possess the social and
cognitive abilities to understand human social parameters and behave
accordingly. However, no such emotion-oriented behavior model is presented yet
in the existing research. The emotion prediction may generate appropriate
agents' behaviors for effective interaction using conversation modality.
Considering the importance of emotions, and behaviors, for an agent's social
interaction, an Emotion-based Behavior model is presented in this paper for
Socio-cognitive artificial agents. The proposed model is implemented using
tweets data trained on multiple models like Long Short-Term Memory (LSTM),
Convolution Neural Network (CNN) and Bidirectional Encoder Representations from
Transformers (BERT) for emotion prediction with an average accuracy of 92%, and
55% respectively. Further, using emotion predictions from CNN-LSTM, the
behavior module responds using facial expressions and gestures using Behavioral
Markup Language (BML). The accuracy of emotion-based behavior predictions is
statistically validated using the 2-tailed Pearson correlation on the data
collected from human users through questionnaires. Analysis shows that all
emotion-based behaviors accurately depict human-like gestures and facial
expressions based on the significant correlation at the 0.01 and 0.05 levels.
This study is a steppingstone to a multi-faceted artificial agent interaction
based on emotion-oriented behaviors. Cognition has significance regarding
social interaction among humans.
