Neuro-Symbolic Hierarchical Reasoning: Bridging Connectionist and Logical AI for Complex Problem Solving

Abstract

This paper introduces a novel approach to complex problem-solving by integrating neuro-symbolic techniques within a hierarchical reasoning framework. We address the limitations of purely connectionist and purely symbolic AI systems by developing a hybrid architecture that leverages the strengths of both paradigms. The core concept involves using neural networks for perception, pattern recognition, and learning, while employing symbolic logic for reasoning, planning, and knowledge representation. The hierarchical structure allows for abstraction and decomposition of complex problems into manageable sub-problems, which are then solved at different levels of abstraction. We present a detailed methodology for designing and implementing such systems, along with experimental results that demonstrate the effectiveness of our approach on several challenging tasks, including automated planning, commonsense reasoning, and visual question answering. The results indicate that our neuro-symbolic hierarchical reasoning framework significantly outperforms both purely connectionist and purely symbolic approaches in terms of accuracy, efficiency, and robustness. We conclude by discussing the broader implications of our work and outlining directions for future research in the field of neuro-symbolic AI.