Dynamic Symbolic Computation via Temporal Logic and Reinforcement Learning

Abstract

This paper presents a novel approach to dynamic symbolic computation by integrating temporal logic reasoning with reinforcement learning. The core idea is to enable a system to adaptively learn and optimize its symbolic representations through a feedback mechanism. We introduce an agent that utilizes temporal logic constraints to guide its manipulation of symbolic representations, and reinforcement learning to optimize its actions based on the success of its reasoning. The system learns to refine its symbolic structures, improving its ability to solve complex problems. This work addresses the limitations of traditional symbolic computation by introducing a dynamic, learning-based framework. The proposed system demonstrates the potential for enhanced reasoning capabilities in domains where symbolic representations are crucial, but static definitions may not be sufficient for optimal performance. The key contributions lie in the synergistic combination of temporal logic's constraint satisfaction capabilities with reinforcement learning's ability to learn optimal strategies. The system's performance is evaluated through simulated scenarios, showcasing the effectiveness of this hybrid approach.