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Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

EasyChair Preprint no. 244

16 pagesDate: June 9, 2018


Over these 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 perform 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

Keyphrases: Answer Set Programming, Handwritten Digit Recognition, Inductive Logic Programming, Question Answering, semantic parsing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Arindam Mitra and Chitta Baral},
  title = {Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples},
  howpublished = {EasyChair Preprint no. 244},
  doi = {10.29007/7kp7},
  year = {EasyChair, 2018}}
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