Neuro-Symbolic Reasoning with Temporal Logic and Neural Networks

Abstract

This paper explores a novel approach to reasoning about temporal relationships by integrating temporal logic with neural networks. The core idea is to train a neural network to understand and apply the rules of temporal logic, thereby enabling it to reason more robustly and reliably about events and their temporal dependencies. We propose a mechanism where the neural network learns both the syntax and semantics of temporal logic expressions. This allows the system to effectively represent and manipulate temporal constraints, ultimately leading to improved reasoning performance compared to purely neural or purely symbolic approaches. The work highlights the potential for creating hybrid systems that leverage the strengths of both formal logic and deep learning, offering a path toward more explainable and trustworthy AI systems for temporal reasoning tasks. The key contribution lies in the formalization of temporal reasoning within a neural network framework, providing a structured method for encoding temporal relationships.