Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples. Issue 3 (10th August 2018)

• Record Type: Journal Article
• Authors: MITRA, ARINDAM; BARAL, CHITTA
• Editors: Dal Palu, Alessandro; Tarau, Paul
• Is Part Of: Theory and practice of logic programming. Volume 18:Issue 3/4(2018)
• Page Start: 623
• Page End: 637
• Publication Date: 2018-08-10
• Subjects: Inductive Logic Programming, -- Answer Set Programming, -- Question Answering, -- Handwritten Digit Recognition, -- Context Dependent Learning; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068418000248
• Languages: English
• ISSNs: 1471-0684

Abstract

Abstract: 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 athttps://goo.gl/KdWAcV .