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

Authors

  • Jonathan C.H. Tong National Taiwan University
  • Yung-Fong Hsu National Taiwan University
  • Churn-Jung Liau Academia Sinica

DOI:

https://doi.org/10.1609/aaaiss.v3i1.31288

Keywords:

Affective Artificial Intelligence, Neural-Symbolic Computing, Goal-direct Decision Making, Dual-process, Causal Reinforcement Learning, Bayesian Neural Network, Convolutional Neural Network

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.
AAAI Spring Symposium 2024 Proceedings Cover

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Published

2024-05-20

Issue

Vol. 3 No. 1: Proceedings of the AAAI 2024 Spring Symposium Series

Section

Symposium on Human-Like Learning