Dynamic Topological Semantic Learning (DTSL)

Abstract

This paper introduces Dynamic Topological Semantic Learning (DTSL), a novel approach to semantic understanding based on interactive observation and dynamic topological graph construction. The core claim of DTSL is that a system can automatically learn and build semantic relationships between objects by observing and simulating their interactions, dynamically adjusting its topology to reflect these evolving relationships. DTSL leverages a combination of reinforcement learning and graph neural networks to achieve this. The system begins with an unstructured graph and utilizes observation units (e.g., visual sensors, text analyzers) to collect data on object interactions. A reinforcement learning agent then iteratively modifies the graph's connections – adding or removing edges – to maximize performance on specified tasks, such as relationship prediction or instruction execution. A graph neural network (GNN) represents and reasons about the graph structure, guiding the connection adjustments. Over time, DTSL constructs a dynamic topology that directly reflects the semantic relationships within the observed environment. This approach contrasts with traditional methods relying on pre-defined knowledge graphs or manually annotated semantic relations, offering a more adaptive and flexible solution.