Learning and programming in classifier systems

Both symbolic and subsymbolic models contribute important insights to our understanding of intelligent systems. Classifier systems are low-level learning systems that are also capable of supporting representations at the symbolic level. In this paper, the authors explore in detail the issues surrounding the integration of programmed and learned knowledge in classifier-system representations, including comprehensibility, ease of expression, explanation, predictability, robustness, redundancy, stability, and the use of analogical representations. They also examine how these issues speak to the debate between symbolic and subsymbolic paradigms.