Nonmonotonic abductive inductive learning

Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and negative examples with respect to background knowledge expressed as logic programs. Negation as Failure (NAF) is a key feature of logic programming which provides a means for nonmonotonic commonsense re...

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Bibliographic Details
Published in:Journal of applied logic Vol. 7; no. 3; pp. 329 - 340
Main Author: Ray, Oliver
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2009
Subjects:
Abduction
Induction
Logic programming
Nonmonotonic reasoning
Logic programming
Induction
Nonmonotonic reasoning
Abduction
ISSN:1570-8683, 1570-8691
Online Access:Get full text
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Summary:Inductive Logic Programming (ILP) is concerned with the task of generalising sets of positive and negative examples with respect to background knowledge expressed as logic programs. Negation as Failure (NAF) is a key feature of logic programming which provides a means for nonmonotonic commonsense reasoning under incomplete information. But, so far, most ILP research has been aimed at Horn programs which exclude NAF, and has failed to exploit the full potential of normal programs that allow NAF. By contrast, Abductive Logic Programming (ALP), a related task concerned with explaining observations with respect to a prior theory, has been well studied and applied in the context of normal logic programs. This paper shows how ALP can be used to provide a semantics and proof procedure for nonmonotonic ILP that utilises practical methods of language and search bias to reduce the search space. This is done by lifting an existing method called Hybrid Abductive Inductive Learning (HAIL) from Horn clauses to normal logic programs. To demonstrate its potential benefits, the resulting system, called XHAIL, is applied to a process modelling case study involving a nonmonotonic temporal Event Calculus (EC).
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ISSN:1570-8683
1570-8691
DOI:10.1016/j.jal.2008.10.007