Neuro-Symbolic Logic Programming with Dynamic Knowledge Graph Construction

Abstract

This paper proposes a novel approach to logic programming that integrates neural networks with symbolic reasoning through the dynamic construction and evolution of a knowledge graph. The core idea is to leverage the pattern recognition capabilities of neural networks to learn logical rules and subsequently apply these rules to a knowledge graph. A reinforcement learning agent is employed to guide the dynamic update of the knowledge graph, incorporating new evidence and inferred relationships. This system represents a shift from static knowledge representation to a continuously learning and adapting system. The system's performance is evaluated through simulations, demonstrating its ability to acquire and reason with complex rule sets. The key contribution lies in the synergistic combination of neural and symbolic approaches, offering a robust framework for intelligent reasoning and knowledge acquisition. The system aims to address the limitations of traditional logic programming (e.g., difficulty in handling uncertainty and scaling to complex domains) and the limitations of neural networks (e.g., lack of explainability and difficulty in incorporating prior knowledge). The proposed architecture provides a path towards more flexible and adaptable intelligent systems.