A Functional Perspective on Machine Learning via Programmable Induction and Abduction - Cheung, Steven; Darvariu, Victor; Ghica, Dan R.; Muroya, Koko; Rowe, Reuben N. S. (Functional and Logic Programming, 2018)
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.