A Functional Perspective on Machine Learning via Programmable Induction and Abduction. / Cheung, Steven; Davariu, Victor; Ghica, Dan; Muroya, Koko; Rowe, Reuben.

FLOPS 2018: Functional and Logic Programming. Vol. 10818 Springer, 2018. p. 84-98 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

E-pub ahead of print

Documents

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

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
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 34708358