Hierarchical Symbolic Computation for Abstract Reasoning

Abstract

This paper addresses the limitations of current deep learning models in performing abstract symbolic reasoning. The core argument is that deep learning's reliance on pattern recognition hinders its ability to grasp underlying concepts and relationships, leading to a lack of genuine understanding. We propose a novel hierarchical symbolic computation framework designed to overcome these deficiencies. This framework combines the pattern recognition capabilities of deep neural networks with a hierarchical symbolic representation of knowledge. The neural network is tasked with generating symbolic representations from raw input, which are then fed into a symbolic inference engine operating on a hierarchical knowledge graph. This architecture allows for reasoning at multiple levels of abstraction, mimicking the way humans solve complex problems. The key innovation lies in the synergistic integration of these two approaches, fostering a system capable of both learning representations and performing logical deductions. The system's ability to handle ambiguity and context is significantly enhanced through the structured, hierarchical knowledge graph. We outline the system architecture, the training methodology, and the inference process, highlighting the benefits of this hybrid approach. Ultimately, this research contributes to a more robust and flexible approach to artificial intelligence, particularly in domains requiring sophisticated abstract reasoning.