A Functional Perspective on Machine Learning via Programmable Induction and Abduction

Steven Cheung, Victor Davariu, Dan Ghica, Koko Muroya, Reuben Rowe

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Abstract

We present a programming language for machine learning based on the concepts of 'induction' and 'abduction' as encountered in Peirce’s logic of science. We consider the desirable features such a language must have, and we identify the 'abductive decoupling' of parameters as a key general enabler of these features. Both an idealised abductive calculus and its implementation as a PPX extension of OCaml are presented, along with several simple examples.
Original languageEnglish
Title of host publicationFLOPS 2018
Subtitle of host publicationFunctional and Logic Programming
PublisherSpringer
Pages84-98
Number of pages15
Volume10818
ISBN (Electronic)978-3-319-90686-7
ISBN (Print)978-3-319-90685-0
DOIs
Publication statusE-pub ahead of print - 24 Apr 2018

Publication series

NameLecture Notes in Computer Science

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