2009 NonmonotonicAbductiveInductiveL

• (Ray, 2009) ⇒ Oliver Ray. (2009). “Nonmonotonic Abductive Inductive Learning.” In: Applied Logic Journal, 7(3). doi:10.1016/j.jal.2008.10.007

Subject Headings: Abductive Logic Programming Task; Inductive Logic Programming Task.

Cited By

• Google Scholar: ~ 115 Citations
• Semantic Scholar: ~ 83 Citations
• Elsevier ScinceDirect: ~ 66 Citations

Quotes

Author Keywords

• Abduction; Induction; Logic programming; Nonmonotonic reasoning

Abstract

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).

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 NonmonotonicAbductiveInductiveL	Oliver Ray			Nonmonotonic Abductive Inductive Learning				10.1016/j.jal.2008.10.007		2009