Inductive Rule Learning on the Knowledge

We present an approach to learning sets of recursive rules based on analytical inductive programming. We propose that our approach can be used within cognitive architectures to model regularity detection and generalization over experience. Induced rule sets represent the knowledge underlying systematic behavior in complex situations. Such rule sets can explain systematicity and productivity of behavior by applying a learned rule set in structural similar situations. The proposed analytical approach to rule induction provides a generalization mechanism which is based on the given experience. That is, this approach is complementary to evolutionary and other generate-and-test based approaches to rule learning where large spaces of hypothetical rule sets are searched for such a set of rules which covers the regularities in the examples. After presenting our approach we will give a variety of example applications from different problem solving domains and show that the same approach can be applied to rule acquisition for reasoning and natural language processing.