An Exploring Study on Building Affective Artificial Intelligence by Neural-Symbolic Computing (Extended Abstract)

This short paper is the status report of a project in progress. We aim to model human-like agents' decision-making behaviors under risks with neural-symbolic approach. Our model integrates the learning, reasoning, and emotional aspects of an agent and takes the dual process thinking into consideration when the agent is making a decision. The model construction is based on real behavioral and brain imaging data collected in a lottery gambling experiment. We present the model architecture including its main modules and the interactions between them.