Inductive Learning of Answer Set Programs from Noisy Examples
  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 generalisation of the learning from answer sets framework. We
evaluate our ILASP3 system, both on synthetic and on real datasets, represented
in the new framework. In particular, we show that on many of the datasets
ILASP3 achieves a higher accuracy than other ILP systems that have previously
been applied to the datasets, including a recently proposed differentiable
learning framework.
