A new algorithm to automate inductive learning of default theories

DOI10.1017/S1471068417000333zbMath1422.68029arXiv1707.02693OpenAlexW2963351225MaRDI QIDQ4592724

Elmer Salazar, Farhad Shakerin, Gopal Gupta

Publication date: 8 November 2017

Published in: Theory and Practice of Logic Programming (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1707.02693

zbMATH Keywords

inductive logic programmingmachine learningdefault reasoningcommon-sense reasoningnonmonotonic logic programming

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Logic in artificial intelligence (68T27) Logic programming (68N17)

Related Items (5)

FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data ⋮ FOLD-R++: a scalable toolset for automated inductive learning of default theories from mixed data ⋮ Unnamed Item ⋮ Unnamed Item ⋮ White-box Induction From SVM Models: Explainable AI with Logic Programming

Uses Software

• Galliwasp
• C4.5
• SWI-Prolog

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nonmonotonic abductive inductive learning
• SWI-Prolog
• Knowledge Representation, Reasoning and Declarative Problem Solving
• Induction from answer sets in nonmonotonic logic programs
• Inductive logic programming

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