  Over the years the Artificial Intelligence (AI) community has produced
several datasets which have given the machine learning algorithms the
opportunity to learn various skills across various domains. However, a subclass
of these machine learning algorithms that aimed at learning logic programs,
namely the Inductive Logic Programming algorithms, have often failed at the
task due to the vastness of these datasets. This has impacted the usability of
knowledge representation and reasoning techniques in the development of AI
systems. In this research, we try to address this scalability issue for the
algorithms that learn answer set programs. We present a sound and complete
algorithm which takes the input in a slightly different manner and performs an
efficient and more user controlled search for a solution. We show via
experiments that our algorithm can learn from two popular datasets from machine
learning community, namely bAbl (a question answering dataset) and MNIST (a
dataset for handwritten digit recognition), which to the best of our knowledge
was not previously possible. The system is publicly available at
https://goo.gl/KdWAcV. This paper is under consideration for acceptance in
TPLP.
