Neuro-Symbolic Symbiosis Architecture: A Dynamic Approach to Enhanced Reasoning and Learning

Abstract

This paper introduces the Neuro-Symbolic Symbiosis Architecture (NSSA), a novel framework designed to enhance reasoning and learning capabilities by dynamically adjusting the interaction between neural networks and symbolic knowledge bases. The core claim is that by modulating the interaction strength between these two components, we can achieve more efficient and robust inference. NSSA employs a multi-layered architecture comprising a neural inference layer for initial pattern recognition and representation learning, a symbolic knowledge base layer for storing structured knowledge, and a symbiotic engine—a reinforcement learning-based controller—to dynamically adjust the flow of information between these layers. The engine's objective is to minimize inference error while maximizing knowledge base utilization. Unlike existing neuro-symbolic methods that often rely on static structures or fixed interaction methods, NSSA leverages reinforcement learning for a highly dynamic and adaptive symbiotic relationship, potentially leading to improvements in generalization, interpretability, and efficiency. The presented architecture offers a shift away from the traditional "neural network vs. symbolic knowledge" dichotomy, emphasizing the mutual dependence and synergistic interaction between the two representation paradigms.