Hyper Least General Generalization and its Application to Higher-Order Concept Learning

We propose a simple extension to Popplestone and Plotkin's concept of Least General Generalization (LGG), in order to generalize literals with diierent predicates. We call this algorithm Hyper Least General Generalization (HLGG). We discuss the importance of HLGG's ability to do predicate invention, and explore the relationship between HLGG and the folding operation of program transformation. Using the inductive logic programming system GOLEM as a foundation , we apply HLGG to a problem which involves higher order concept learning, and describe an example which extracts a higher order concept like transitivity. Finally, we compare HLGG to Higher Order LGG, as proposed by Feng and Muggleton.