Based on Neural-Symbolic AI for Causal Reasoning and Decision-Making

Abstract

This paper presents a novel approach to causal reasoning and decision-making by integrating the strengths of neural and symbolic artificial intelligence. The core idea is to leverage the pattern recognition capabilities of neural networks to learn causal relationships while utilizing symbolic reasoning for logical inference and decision-making processes. This fusion aims to create a system capable of generating more reliable and interpretable intelligent decisions. We introduce a framework where neural networks represent observational data and learn underlying causal structures, which are then formalized and manipulated using symbolic logic for reasoning. The resulting system offers a significant advancement over traditional approaches by addressing the complexities inherent in causal inference and ultimately enhancing the trustworthiness and explainability of intelligent decision-making systems. The paper details the architecture of the proposed system, the methods used for neural-symbolic integration, and outlines potential applications. ---