Iterative Learning of Answer Set Programs from Context Dependent
  Examples
  In recent years, several frameworks and systems have been proposed that
extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP)
paradigm. In ILP, examples must all be explained by a hypothesis together with
a given background knowledge. In existing systems, the background knowledge is
the same for all examples; however, examples may be context-dependent. This
means that some examples should be explained in the context of some
information, whereas others should be explained in different contexts. In this
paper, we capture this notion and present a context-dependent extension of the
Learning from Ordered Answer Sets framework. In this extension, contexts can be
used to further structure the background knowledge. We then propose a new
iterative algorithm, ILASP2i, which exploits this feature to scale up the
existing ILASP2 system to learning tasks with large numbers of examples. We
demonstrate the gain in scalability by applying both algorithms to various
learning tasks. Our results show that, compared to ILASP2, the newly proposed
ILASP2i system can be two orders of magnitude faster and use two orders of
magnitude less memory, whilst preserving the same average accuracy. This paper
is under consideration for acceptance in TPLP.
