Toward Statistical Predicate Invention

In the past few years, the statistical relational learning (SRL) community has recognized the importance of combining the strengths of statistical learning and relational learning (also known as inductive logic programming (ILP)), and developed several novel representations, as well as algorithms to learn their parameters and structure. However, the problem of statistical predicate invention (SPI) has so far received little attention in the community. SPI is the discovery of new concepts, properties and relations from data, expressed in terms of the observable ones, using statistical techniques to guide the process and explicitly representing the uncertainty in the discovered predicates. These can in turn be used as a basis for discovering new predicates, which is potentially much more powerful than learning based on a fixed set of simple primitives. Essentially all the concepts used by humans can be viewed as invented predicates, with many levels of discovery between them and the sensory percepts they are ultimately based on.