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