Journal
INDUCTIVE LOGIC PROGRAMMING, ILP 2014
Volume 9046, Issue -, Pages 33-48Publisher
SPRINGER-VERLAG BERLIN
DOI: 10.1007/978-3-319-23708-4_3
Keywords
ILP; ALP; ASP; Metabolic networks; Completion; Revision
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Funding
- EPSRC [EP/K035959/1] Funding Source: UKRI
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This paper introduces a new open-source implementation of a nonmonotonic learning method called XHAIL and shows how it can be used for abductive and inductive inference on metabolic networks that are many times larger than could be handled by the preceding prototype. We summarise several implementation improvements that increase its efficiency and we introduce an extended form of language bias that further increases its usability. We investigate the system's scalability in a case study involving real data previously collected by a Robot Scientist and show how it led to the discovery of an error in a whole-organism model of yeast metabolism.
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