Symbolic AI with Dynamic Rule Generation and Refinement

Abstract

This paper presents a novel approach to symbolic artificial intelligence (AI) that addresses the limitations of traditional, manually-engineered rule-based systems. The core challenge in symbolic AI has historically been the extensive and often laborious process of crafting and maintaining a comprehensive rule set. Existing systems frequently exhibit brittleness and a lack of adaptability to changing environments. To mitigate these issues, we propose a system architecture leveraging reinforcement learning (RL) to dynamically generate and refine a symbolic rule base. The system operates by employing an RL agent that observes the system's performance and iteratively adjusts the rules to optimize its behavior. This dynamic approach significantly reduces the manual effort required for rule engineering while enhancing the system's robustness and adaptability. We detail the key components of this system, including the rule representation, the RL agent's reward function, and the rule modification mechanisms. The system's core claim is that a dynamically generated and refined rule base offers a more robust and adaptable solution compared to static, manually-engineered rule sets. The system's ability to learn and adapt to data-driven feedback represents a significant advancement in the field of symbolic AI. The presented method demonstrates a pathway toward creating AI systems that can autonomously evolve their knowledge representation, ultimately leading to greater flexibility and efficiency.