Neuro-Symbolic Reasoning with Bayesian Networks

Abstract

This paper proposes a novel approach to reasoning in complex domains by integrating the strengths of neural networks and Bayesian networks. The core idea is to leverage neural networks for feature extraction and symbolic representation learning, subsequently feeding these representations into a Bayesian network to facilitate probabilistic inference and decision-making. This hybrid system aims to overcome limitations inherent in purely neural or purely symbolic approaches, leading to more robust and explainable reasoning capabilities. The paper details the architecture, the learning process, and the inference mechanism of this neuro-symbolic framework. We demonstrate the potential of this approach through a conceptual design, focusing on the integration of learned symbolic knowledge into a Bayesian network for improved reasoning performance. The key contribution lies in the systematic combination of neural representation learning with Bayesian network inference, providing a path toward more reliable and interpretable AI systems.