#
Learning Inference Rules from Data

##
Chiaki Sakama, Katsumi Inoue and Tony Ribeiro

*
KI - Kunstliche Intelligenz*, Vol.33(3), pages 267-278, 2019.
## Abstract

This 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 A provides training examples that consist of
formulas S and their logical consequences T.
Then a machine M builds an axiomatic system that makes T a consequence of S.
Alternatively, in the absence of an agent A, a machine 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.

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