A Novel Weight-Driven ATN-Based SQL Sentence Generator to Accommodate AI-Based Reinforcement Learning

This paper presents a novel approach for generating SQL queries through a weight-driven framework using a modified ATN of ANTLR4's runtime components. Our objective is to enhance ATN capabilities for SQL generation by incorporating the functionality to accommodate adaptive learning solutions. We successfully designed and implemented a system that assigns weights to ATN transitions, including token weight assignment when presented with multiple valid tokens to choose from whilst traversing set-transitions. These weights have interfaces for dynamic adjustments based on heuristics and user-defined strategies. Our methodology involves modifying ANTLR4's core components to include weight management and traversal algorithms. We leverage heuristics to guide weight adjustments, addressing loop structures and recursive depth control in a system controlled by weights. Additionally, we establish mechanisms for weight persistence and optimization. Experimental evaluation using a simplistic SQL grammar demonstrates the effectiveness of our approach. We observe that weights can steer the parsing process towards desired outcomes, and that convergence occurs as the exploration-exploitation balance is optimized through parameter tuning. This research lays the groundwork for integrating re-inforcement learning with our weight-driven ATN system. This holds promise for tackling complex challenges in structured data analysis that might not be readily apparent through human inspection alone. While our current work primarily focuses on heuristics, future efforts will explore the next stage of our research to further enhance the decision-making capabilities of our framework using reinforcement learning,