A functional perspective on machine learning via programmable induction and abduction

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

Authors

Colleges, School and Institutes

External organisations

  • University of Birmingham
  • Kyoto University, Kyoto, Japan
  • University of Kent

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.

Details

Original languageEnglish
Title of host publicationFunctional and Logic Programming
Subtitle of host publication14th International Symposium, FLOPS 2018, Nagoya, Japan, May 9–11, 2018, Proceedings
EditorsJohn P. Gallagher, Martin Sulzmann
Publication statusPublished - 9 May 2018
Event14th International Symposium on Functional and Logic Programming, (FLOPS 2018) - Nagoya, Japan
Duration: 9 May 201811 May 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10818
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Symposium on Functional and Logic Programming, (FLOPS 2018)
CountryJapan
CityNagoya
Period9/05/1811/05/18