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Learning Inference by Induction

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Chiaki Sakama, Tony Ribeiro and Katsumi Inoue

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Proceedings of the 25th International Conference on Inductive Logic Programming (ILP 2015),
Lecture Notes in Artificial Intelligence 9575, Springer-Verlag, pages 183-199, 2015.
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## Abstract

This paper studies *learning inference* by induction.
We first consider the problem of learning logical inference rules.
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 transitions.
Next we consider the problem of learning non-logical inference rules.
We address three case studies for learning abductive inference, frame axioms and conversational implicature
by induction.
The current study provides a preliminary approach to the problem of learning inference
to which little attention has been paid in machine learning and ILP.

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