In recent years, non-monotonic inductive logic programming has received growing
interest. Specifically, several new learning frameworks and algorithms have
been introduced for learning under the answer set semantics, allowing the
learning of common-sense knowledge involving defaults and exceptions, which are
essential aspects of human reasoning. In this paper, we present a
noise-tolerant generalization of the learning from answer sets
framework. We evaluate our ILASP3 system, both on synthetic and on real
data sets, represented in the new framework. In particular, we show that on many
of the data sets ILASP3 achieves a higher accuracy than other systems that
have previously been applied to the data sets, including a recently proposed
differentiable framework for inductive logic programming.
