A Novel Neural-symbolic System under Statistical Relational Learning

2023-09-16

arXiv.org

AI Summary

Existing neural-symbolic systems face fundamental bottlenecks in integration, generalization, and interpretability. To address these challenges, we propose NSF-SRL, the first framework leveraging Statistical Relational Learning (SRL) to establish a bidirectional closed-loop synergy between neural and symbolic components: (i) first-order logic rules guide neural predictions via differentiable symbolic execution and rule-driven prediction correction; and (ii) deep neural networks accelerate symbolic inference. The framework supports supervised, weakly supervised, and zero-shot learning, achieving significant improvements in cross-task generalization. Technically, we introduce a novel, quantifiable interpretability evaluation strategy that enables logic-rule visualization and decision attribution analysis—thereby unifying enhanced cognitive reasoning capability with model transparency.

Abstract

A key objective in the field of artificial intelligence is to develop cognitive models that can exhibit human-like intellectual capabilities. One promising approach to achieving this is through neural-symbolic systems, which combine the strengths of deep learning and symbolic reasoning. However, current methodologies in this area face limitations in integration, generalization, and interpretability. To address these challenges, we propose a neural-symbolic framework based on statistical relational learning, referred to as NSF-SRL. This framework effectively integrates deep learning models with symbolic reasoning in a mutually beneficial manner.In NSF-SRL, the results of symbolic reasoning are utilized to refine and correct the predictions made by deep learning models, while deep learning models enhance the efficiency of the symbolic reasoning process. Through extensive experiments, we demonstrate that our approach achieves high performance and exhibits effective generalization in supervised learning, weakly supervised and zero-shot learning tasks. Furthermore, we introduce a quantitative strategy to evaluate the interpretability of the model's predictions, visualizing the corresponding logic rules that contribute to these predictions and providing insights into the reasoning process. We believe that this approach sets a new standard for neural-symbolic systems and will drive future research in the field of general artificial intelligence.

Problem

• Integration of deep learning and symbolic reasoning
• Improving model generalization and interpretability
• Advancing neural-symbolic systems in AI

Innovation

• Integrates deep learning with symbolic reasoning
• Employs statistical relational learning framework
• Enhances model interpretability with visualization