Strength or Accuracy: Credit Assignment in Learning Classifier Systems

Classifier systems offer a unique solution to machine learning challenges through the automated creation of condition/action rules. The XCS system, introduced by Stewart Wilson in 1995, represents a significant advancement by calculating rule value based on accuracy rather than reward, distinguishing it from earlier strength-based systems. This approach enhances credit assignment, allowing for improved policy learning and generalization. As a Q-learning system, XCS aggregates states and actions to optimize action selection, marking a notable evolution in reinforcement learning methodologies.