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A new algorithm to automate inductive learning of default theories*

Published online by Cambridge University Press:  23 August 2017

FARHAD SHAKERIN Open the ORCID record for FARHAD SHAKERIN [Opens in a new window] ,
ELMER SALAZAR  and
GOPAL GUPTA
FARHAD SHAKERIN
Affiliation:
The University of Texas at Dallas, Texas, USA (e-mails: [email protected], [email protected], [email protected])
ELMER SALAZAR
Affiliation:
The University of Texas at Dallas, Texas, USA (e-mails: [email protected], [email protected], [email protected])
GOPAL GUPTA
Affiliation:
The University of Texas at Dallas, Texas, USA (e-mails: [email protected], [email protected], [email protected])
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Abstract

In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. Therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant improvement over traditional approaches based on inductive logic programming. Under consideration for acceptance in TPLP.

Keywords

inductive logic programmingnon-monotonic logic programmingdefault reasoningcommon-sense reasoningmachine learning
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 17 , Special Issue 5-6: 33rd International Conference on Logic Programming , September 2017 , pp. 1010 - 1026
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

*

Authors are partially supported by NSF Grant No. 1423419.

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