We present a novel approach to non-monotonic ILP and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ILP systems based on Inverse Entailment and is the first top-down ILP system that allows background theories and hypotheses to be normal logic programs. The approach relies on mapping an ILP problem into an equivalent ALP one. This enables the use of established ALP proof procedures and the specification of richer language bias with integrity constraints. The mapping provides a principled search space for an ILP problem, over which an abductive search is used to compute inductive solutions.
@InProceedings{corapi_et_al:LIPIcs.ICLP.2010.54, author = {Corapi, Domenico and Russo, Alessandra and Lupu, Emil}, title = {{Inductive Logic Programming as Abductive Search}}, booktitle = {Technical Communications of the 26th International Conference on Logic Programming}, pages = {54--63}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-939897-17-0}, ISSN = {1868-8969}, year = {2010}, volume = {7}, editor = {Hermenegildo, Manuel and Schaub, Torsten}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2010.54}, URN = {urn:nbn:de:0030-drops-25838}, doi = {10.4230/LIPIcs.ICLP.2010.54}, annote = {Keywords: Inductive Logic Programming, Abductive Logic Programming, Non-monotonic Reasoning} }
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