Interactive inductive learning

We propose an interactive probabilistic inductive learning model which defines a feedback relationship between the user and the learning program. We extend previously described learning algorithms to a conditional model previously described by the authors, and formulate our Conditional Probabilistic Learning Algorithm (CPLA), applying conditions as introduced by Wasilewska to a probabilistic version of the work of Wong and Wong. We propose the Condition Suggestion Algorithm (CSA) as a way to use the syntactic knowledge in the system to generalize the family of decision rules. We also examine the semantic knowledge of the system implied by the suggested conditions and analyse the effects of conditions on the system. CPLA/CSA has been implemented by the first author and was used to generate the examples presented.