Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples
This addresses a bottleneck in knowledge representation and reasoning for AI systems, though it appears incremental as it builds on existing inductive logic programming methods.
The paper tackles the scalability issue of learning answer set programs from large datasets by introducing a sound and complete algorithm that enables learning from datasets like bAbl and MNIST, which was previously not possible.
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.