Chapter 33. Learning Classifier Systems
Learning classifier systems (LCSs) are best understood as a combination of three popular AI techniques: genetic algorithms, rule-based systems, and reinforcement learning. LCSs are capable of learning to predict the best action based on the current situation (a classification problem).
Rule-based systems require significant involvement from a human designer—the knowledge acquisition bottleneck. Instead, a system could use the creativity of genetic algorithms and the guidance of reinforcement learning to find the right behavior autonomously—as most animals do. By using information gathered from the environment and a high-level measure of performance as feedback, it's theoretically possible for a system to find the most appropriate set of rules. In practice, the adaptation can be difficult to apply at runtime in a realistic fashion, but LCSs have their advantages compared to other approaches (for instance, they're automated).
This chapter covers the following topics: