基于神经符号人工智能的知识推理

Abstract

This paper explores a novel approach to artificial intelligence by integrating the strengths of neural networks and symbolic reasoning. The core idea is to leverage the pattern recognition capabilities of neural networks for knowledge extraction and representation, coupled with the logical deduction and inference capabilities of symbolic reasoning systems. This hybrid architecture aims to construct AI systems that not only possess high accuracy but also offer enhanced interpretability and reliability. The system utilizes a neural network to process raw input data and generate a structured knowledge graph, which is then subjected to symbolic reasoning algorithms to derive conclusions and make decisions. The proposed method addresses the limitations of existing neural AI approaches that often lack explicit knowledge representation and reasoning mechanisms, leading to a lack of transparency and difficulty in debugging. This research contributes to the development of more robust and trustworthy AI systems by grounding AI decisions in a formally represented knowledge base. The evaluation framework will focus on assessing the system's accuracy, interpretability, and robustness across various reasoning tasks.