@article{Broda:2016:10.1017/S1471068416000351,
author = {Broda, KB and Law, M and Russo, A},
doi = {10.1017/S1471068416000351},
journal = {Theory and Practice of Logic Programming},
pages = {834--848},
title = {Iterative Learning of Answer Set Programs with Context Dependent Examples},
url = {http://dx.doi.org/10.1017/S1471068416000351},
volume = {16},
year = {2016}
}

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TY  - JOUR
AB  - In recent years, several frameworks and systems have been proposed that extend InductiveLogic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examplesmust all be explained by a hypothesis together with a given background knowledge. In existingsystems, the background knowledge is the same for all examples; however, examples may becontext-dependent. This means that some examples should be explained in the context ofsome information, whereas others should be explained in different contexts. In this paper, wecapture this notion and present a context-dependent extension of the Learning from OrderedAnswer Sets framework. In this extension, contexts can be used to further structure thebackground knowledge. We then propose a new iterative algorithm, ILASP2i, which exploitsthis feature to scale up the existing ILASP2 system to learning tasks with large numbersof examples. We demonstrate the gain in scalability by applying both algorithms to variouslearning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2isystem can be two orders of magnitude faster and use two orders of magnitude less memory,whilst preserving the same average accuracy
AU  - Broda,KB
AU  - Law,M
AU  - Russo,A
DO  - 10.1017/S1471068416000351
EP  - 848
PY  - 2016///
SN  - 1475-3081
SP  - 834
TI  - Iterative Learning of Answer Set Programs with Context Dependent Examples
T2  - Theory and Practice of Logic Programming
UR  - http://dx.doi.org/10.1017/S1471068416000351
UR  - http://hdl.handle.net/10044/1/42063
VL  - 16
ER  - 

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