This folder contains a Logtalk port of metagol, an inductive logic programming (ILP) system based on meta-interpretive learning available from:

See the original code git repo for details and bibliography on Metagol and ILP.

The port allows any number of datasets to be loaded simultaneously with per-dataset learning options. A dataset is simply wrapped in an object that extends and is expanded by the metagol object as illustrated by the ported examples.

Both the original code and the port requires the coroutining when/2 predicate, which is only available in some backend Prolog systems. The port currently supports ECLiPSe, LVM, SICStus Prolog, SWI-Prolog, and YAP. It can be used on both POSIX and Windows operating-systems.

The examples are ported from the original Metagol distribution. Some of the examples are taken from the following paper (with the original Prolog examples source code files made available by MystikNinja):

    author    = {Richard Evans and Edward Grefenstette},
    title     = {Learning Explanatory Rules from Noisy Data},
    journal   = {J. Artif. Intell. Res.},
    volume    = {61},
    pages     = {1--64},
    year      = {2018},
    url       = {},
    doi       = {10.1613/jair.5714},
    timestamp = {Mon, 21 Jan 2019 15:01:17 +0100},
    biburl    = {},
    bibsource = {dblp computer science bibliography,}

The paper can be downloaded at

For sample queries, please see the SCRIPT.txt file.

API documentation

Open the ../../docs/library_index.html#metagol link in a web browser.


To load all entities in this port, load the loader.lgt file:

| ?- logtalk_load(metagol(loader)).


To test this port predicates, load the tester.lgt file:

| ?- logtalk_load(metagol(tester)).

There are three lengthy tests that only run when the tests are being run manually instead of automatically.