Dynamic Semantic Network Generator

Abstract

This paper introduces a Dynamic Semantic Network Generator (DSNG), a novel system designed to automatically construct and maintain semantic networks representing complex, dynamic systems. The core of the DSNG is a reinforcement learning (RL) framework that adapts the network's structure and connections in response to changes in system states and external events. Unlike traditional static semantic networks, the DSNG allows for real-time semantic understanding and evolution, offering a more accurate and flexible representation of complex systems. The system utilizes a state representation, action space, and reward function to learn optimal network configurations. The key innovation lies in the dynamic adjustment mechanism, leveraging RL to continuously refine the network's topology and weights, mirroring the evolving relationships within the system under observation. This approach addresses the limitations of static models, providing a robust solution for analyzing and understanding systems where relationships are not fixed but constantly shifting. The system's performance is evaluated through simulations, demonstrating its ability to capture and adapt to complex temporal dependencies.