Title: Self-Organizing Constraint Networks for Dynamic Data Structures

Abstract

This paper introduces a novel approach to data structure design leveraging Self-Organizing Constraint Networks (SOCN). SOCN networks dynamically reorganize themselves based on observed data patterns, offering a highly adaptable solution for a wide range of applications. We propose a reinforcement learning framework to train an agent that learns to restructure the constraint network, resulting in a system capable of automatically optimizing data structure performance. The core mechanism focuses on learning the optimal rearrangement of constraint nodes and edges to maximize efficiency and resilience. The system's adaptability stems from its continuous learning process, making it a significant advancement over static constraint network approaches. This work presents a comprehensive investigation of the system's behavior, demonstrating its effectiveness through simulations and a preliminary experimental evaluation.