Modeling the Evolution of Knowledge and Reasoning in Learning Systems

How do reasoning systems that learn evolve over time? Characterizing the evolution of these systems is important for understanding their limitations and gaining insights into the interplay between learning and reasoning. We describe an inverse ablation model for studying how learning and reasoning interact: Create a small knowledge base by ablation, and incrementally re-add facts, collecting snapshots of reasoning performance of the system to measure properties of interest. Experiments with this model suggest that different concepts show different rates of growth, and that the density of facts is an important parameter for modulating the rate of learning.