Repeat Learning Using Predicate Invention

Most of machine learning is concerned with learning a single concept from a sequence of examples. In repeat learning the teacher chooses a series of related concepts randomly and independently from a distribution D. A nite sequence of examples is provided for each concept in the series. The learner does not initially know D, but progressively updates a posterior estimation of D as the series progresses. This paper considers predicate invention within Inductive Logic Programming as a mechanism for updating the learner's estimation of D. A new predicate invention mechanism implemented in Progol4.4 is used in repeat learning experiments within a chess domain. The results indicate that signiicant performance increases can be achieved. The paper develops a Bayesian framework and demonstrates initial theoretical results for repeat learning .