A new algorithm to automate inductive learning of default theories*. Issue 5 (23rd August 2017)

• Record Type: Journal Article
• Authors: SHAKERIN, FARHAD; SALAZAR, ELMER; GUPTA, GOPAL
• Editors: Rocha, Ricardo; Cao Son, Tran
• Is Part Of: Theory and practice of logic programming. Volume 17:Issue 5/6(2017)
• Page Start: 1010
• Page End: 1026
• Publication Date: 2017-08-23
• Subjects: inductive logic programming, -- non-monotonic logic programming, -- default reasoning, -- common-sense reasoning, -- machine learning; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068417000333
• Languages: English
• ISSNs: 1471-0684

Abstract

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.