A Soft Version of Predicate Invention Based on Structured Sparsity.

In predicate invention (PI), new predicates are introduced into a logical theory, usually by rewriting a group of closely-related rules to use a common invented predicate as a \"subroutine\". PI is difficult, since a poorly-chosen invented predicate may lead to error cascades. Here we suggest a \"soft\" version of predicate invention: instead of explicitly creating new predicates, we implicitly group closely-related rules by using structured sparsity to regularize their parameters together. We show that soft PI, unlike hard PI, consistently improves over previous strong baselines for structure-learning on two large-scale tasks.