A functional perspective on machine learning via programmable induction and abduction

Steven Cheung; Victor Darvariu; Dan R. Ghica; Koko Muroya; Reuben N.S. Rowe

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A functional perspective on machine learning via programmable induction and abduction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2018) 10818 LNCS 84-98

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

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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.

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Cheung, S., Darvariu, V., Ghica, D. R., Muroya, K., & Rowe, R. N. S. (2018). A functional perspective on machine learning via programmable induction and abduction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10818 LNCS, pp. 84–98). Springer Verlag. https://doi.org/10.1007/978-3-319-90686-7_6

A functional perspective on machine learning via programmable induction and abduction

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