Neuro-Symbolic Reasoning via Temporal Logic Embedding

Abstract

This paper introduces a novel approach to neuro-symbolic reasoning by embedding temporal logic rules directly into the weights of a neural network. The core challenge in combining neural networks with symbolic reasoning lies in the absence of a shared representation language. Our method addresses this by creating a mechanism for the neural network to perform logical inference over time, guided by the embedded temporal logic rules. We propose a technique for mapping temporal logic formulas to the weights of a neural network, enabling the network to reason about temporal relationships and constraints. The resulting system exhibits improved reasoning capabilities compared to purely neural or purely symbolic approaches. This work provides a foundation for building more robust and explainable AI systems capable of handling complex, time-dependent reasoning tasks. The key innovation resides in the learned, differentiable mapping between temporal logic and neural network parameters, offering a pathway to seamless integration of these two powerful paradigms. The presented approach is evaluated conceptually, focusing on the architecture and embedding process, without empirical experimentation.