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Machine learning functional programs

Supervisor

Suitable for

MSc in Computer Science
Mathematics and Computer Science, Part C
Computer Science and Philosophy, Part C
Computer Science, Part C
Computer Science, Part B

Abstract

"Meta-interpretive learning (MIL) [1,2] is a state-of-the-art program induction system that learns computer programs from input/output examples of a target program. MIL is based on inductive logic programming, and learns logic programs (Prolog and ASP programs). The goal of this project is to see whether the MIL techniques can be adapted or translated into a functional paradigm as to learn functional programs. This work is a mix of theory, implementation, experimentation, but most of the work will be developing the system.

[1] A. Cropper and S.H. Muggleton. Learning higher-order logic programs through abstraction and invention. In Proceedings of the 25th International Joint Conference Artificial Intelligence (IJCAI 2016), pages 1418-1424. IJCAI, 2016. [2] S.H. Muggleton, D. Lin, and A. Tamaddoni-Nezhad. Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited. Machine Learning, 100(1):49-73, 2015."

Prerequisites: comfortable with functional programming and preferably comfortable with logic programming