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

Published online by Cambridge University Press:  10 August 2018

ARINDAM MITRA Open the ORCID record for ARINDAM MITRA [Opens in a new window]  and
CHITTA BARAL
ARINDAM MITRA
Affiliation:
Arizona State University (e-mail: amitra7@asu.edu, chitta@asu.edu)
CHITTA BARAL
Affiliation:
Arizona State University (e-mail: amitra7@asu.edu, chitta@asu.edu)
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Abstract

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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.

Keywords

Inductive Logic ProgrammingAnswer Set ProgrammingQuestion AnsweringHandwritten Digit RecognitionContext Dependent Learning
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 18 , Special Issue 3-4: 34th International Conference on Logic Programming , July 2018 , pp. 623 - 637
Creative Commons
This is a work of the U.S. Government and is not subject to copyright protection in the United States.
Copyright
Copyright © Cambridge University Press 2018

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