  We consider the problem of learning generalized first-order representations
of concepts from a single example. To address this challenging problem, we
augment an inductive logic programming learner with two novel algorithmic
contributions. First, we define a distance measure between candidate concept
representations that improves the efficiency of search for target concept and
generalization. Second, we leverage richer human inputs in the form of advice
to improve the sample-efficiency of learning. We prove that the proposed
distance measure is semantically valid and use that to derive a PAC bound. Our
experimental analysis on diverse concept learning tasks demonstrates both the
effectiveness and efficiency of the proposed approach over a first-order
concept learner using only examples.
