A Functional Perspective on Machine Learning via Programmable Induction and Abduction

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

doi:10.1007/978-3-319-90686-7_6

A Functional Perspective on Machine Learning via Programmable Induction and Abduction

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

224 Downloads (Pure)

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 language	English
Title of host publication	FLOPS 2018
Subtitle of host publication	Functional and Logic Programming
Publisher	Springer
Pages	84-98
Number of pages	15
Volume	10818
ISBN (Electronic)	978-3-319-90686-7
ISBN (Print)	978-3-319-90685-0
DOIs	https://doi.org/10.1007/978-3-319-90686-7_6
Publication status	E-pub ahead of print - 24 Apr 2018

Publication series

Name	Lecture Notes in Computer Science

Access to Document

10.1007/978-3-319-90686-7_6

Accepted ManuscriptAccepted author manuscript, 332 KB

Cite this

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