A Functional Perspective on Machine Learning via Programmable Induction and Abduction - Research

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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 proceeding › Conference contribution

E-pub ahead of print

Documents

Accepted Manuscript
Accepted author manuscript, 332 KB, PDF document

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 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