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Learning Inference Rules from Data

dc.contributor.authorSakama, Chiaki
dc.contributor.authorInoue, Katsumi
dc.contributor.authorRibeiro, Tony
dc.date.accessioned2021-04-23T09:27:09Z
dc.date.available2021-04-23T09:27:09Z
dc.date.issued2019
dc.description.abstractThis paper considers the possibility of designing AI that can learn logical or non-logical inference rules from data. We first provide an abstract framework for learning logics. In this framework, an agent $${{{\mathcal {A}}}}$$ A provides training examples that consist of formulas S and their logical consequences T . Then a machine $${{{\mathcal {M}}}}$$ M builds an axiomatic system that makes T a consequence of S . Alternatively, in the absence of an agent $$\mathcal{A}$$ A , a machine $${{{\mathcal {M}}}}$$ M seeks an unknown logic underlying given data. We next consider the problem of learning logical inference rules by induction. Given a set S of propositional formulas and their logical consequences T , the goal is to find deductive inference rules that produce T from S . We show that an induction algorithm LF1T , which learns logic programs from interpretation transitions, successfully produces deductive inference rules from input data. Finally, we consider the problem of learning non-logical inference rules. We address three case studies for learning abductive inference, frame axioms, and conversational implicature. Each case study uses machine learning techniques together with metalogic programming.de
dc.identifier.doi10.1007/s13218-019-00597-y
dc.identifier.pissn1610-1987
dc.identifier.urihttp://dx.doi.org/10.1007/s13218-019-00597-y
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/36240
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 33, No. 3
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectInduction
dc.subjectInference rule
dc.subjectMachine learning
dc.titleLearning Inference Rules from Datade
dc.typeText/Journal Article
gi.citation.endPage278
gi.citation.startPage267

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