Neuro-Symbolic Logic Programming with Reinforcement Learning

Abstract

This paper proposes a novel approach to artificial intelligence—Neuro-Symbolic Logic Programming with Reinforcement Learning—designed to address the limitations of current AI techniques. The core idea is to integrate the pattern recognition capabilities of neural networks with the reasoning and explainability offered by symbolic logic programming, guided by reinforcement learning. We present a hybrid system where a neural network learns a high-level representation of a task, translating sensory inputs into abstract concepts. This representation is then fed into a symbolic logic engine, which executes predefined rules and generates plans. Reinforcement learning is utilized to optimize the neural network's representation and the logic engine's rule selection, allowing the system to adapt and improve its performance over time. This approach aims to create AI systems that are not only capable of complex behavior but also provide verifiable, logically sound explanations for their actions. The system's architecture and the interaction between its components are detailed, highlighting the potential for robust and explainable AI. We demonstrate a conceptual framework, outlining the key components and their interplay, and discuss potential future research directions.