基于动态拓扑记忆网络的神经符号推理

Abstract

This paper proposes a novel approach to neural-symbolic reasoning by introducing a Dynamic Topological Memory Network (DTMN). The core idea is to construct a memory network capable of dynamically adjusting its internal topology to mirror the complexity and relationships within the input data. This addresses a key limitation of existing neural-symbolic methods that often rely on static knowledge graphs or predefined rules, struggling with uncertainty and intricate relationships. The DTMN incorporates a graph-based neural architecture where nodes represent concepts or facts, and edges represent their relationships. A "topological learner" dynamically adjusts the network's structure—including adding, removing, or modifying nodes and edges—using techniques like reinforcement learning or evolutionary algorithms, guided by the input data and existing knowledge. A "symbolic reasoning engine" then leverages this evolving topology for logical inference, generating symbolic expressions as output. The significance lies in the adaptive nature of the network's topology, enabling a more flexible and effective reasoning process compared to traditional methods. We demonstrate the potential of this architecture for robust and efficient symbolic reasoning tasks.