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- Title
A new algorithm to automate inductive learning of default theories.
- Authors
SHAKERIN, FARHAD; SALAZAR, ELMER; GUPTA, GOPAL
- 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.
- Subjects
INDUCTIVE logic programming; DEFAULT reasoning; COMMONSENSE reasoning; MACHINE learning; COMPUTER algorithms
- Publication
Theory & Practice of Logic Programming, 2017, Vol 17, Issue 5/6, p1010
- ISSN
1471-0684
- Publication type
Article
- DOI
10.1017/S1471068417000333