A functional perspective on machine learning via programmable induction and abduction

Cheung, Steven, Darvariu, Victor, Ghica, Dan R., Muroya, Koko, Rowe, Reuben (2018) A functional perspective on machine learning via programmable induction and abduction. In: Lecture Notes in Artificial Intelligence. Proceedings of the Fourteenth International Symposium on Functional and Logic Programming (FLOPS 2018) 9-11 May, 2018, Nagoya, Japan. Lecture Notes in Computer Science . Springer, Switzerland ISBN 978-3-319-90686-7. (doi:10.1007/978-3-319-90686-7_6) (KAR id:66447)

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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.

Item Type:	Conference or workshop item (Proceeding)
DOI/Identification number:	10.1007/978-3-319-90686-7_6
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User:	Reuben Rowe
Date Deposited:	19 Mar 2018 10:11 UTC
Last Modified:	05 Nov 2024 11:05 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/66447 (The current URI for this page, for reference purposes)

University of Kent Author Information

Rowe, Reuben.

Creator's ORCID:	https://orcid.org/0000-0002-4271-9078
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