Metagol is an inductive logic programming (ILP) system based on meta-interpretive learning. If you use Metagol for research, please use this citation and cite the relevant paper.

Metagol is written in Prolog and runs with SWI-Prolog.

The following code demonstrates learning the grandparent relation given the mother and father relations as background knowledge:

:- use_module('metagol').

%% metagol settings
body_pred(mother/2).
body_pred(father/2).

%% background knowledge
mother(ann,amy).
mother(ann,andy).
mother(amy,amelia).
mother(linda,gavin).
father(steve,amy).
father(steve,andy).
father(gavin,amelia).
father(andy,spongebob).

%% metarules
metarule([P,Q],[P,A,B],[[Q,A,B]]).
metarule([P,Q,R],[P,A,B],[[Q,A,B],[R,A,B]]).
metarule([P,Q,R],[P,A,B],[[Q,A,C],[R,C,B]]).

%% learning task
:-
  %% positive examples
  Pos = [
    grandparent(ann,amelia),
    grandparent(steve,amelia),
    grandparent(ann,spongebob),
    grandparent(steve,spongebob),
    grandparent(linda,amelia)
  ],
  %% negative examples
  Neg = [
    grandparent(amy,amelia)
  ],
  learn(Pos,Neg).

Running the above program will print the output:

% clauses: 1
% clauses: 2
% clauses: 3
grandparent(A,B):-grandparent_1(A,C),grandparent_1(C,B).
grandparent_1(A,B):-mother(A,B).
grandparent_1(A,B):-father(A,B).

In this program the predicate symbol grandparent_1 is invented (i.e. does not appear in the background knowledge nor in the examples).

Metagol requires metarules as input. Metarules define the form of clauses permitted in a hypothesis and thus the search space.

Metarules are of the form metarule(Name, Subs, Head, Body). An example metarule is:

metarule(chain, [P,Q,R], [P,A,B], [[Q,A,C],[R,C,B]]).

Here the symbols P, Q, and R denote second-order variables (i.e. can unify with predicate symbols). The symbols A, B, and C denote first-order variables (i.e. can unify with constant symbols). Metagol will search for appropriate substitutions for the variables in the second argument of a metarule (Subs).

Users must supply metarules. Deciding which metarules to use is still an open (and hard!) problem. Some work in this area is detailed in the papers:

• A. Cropper and S. Tourret. Logical minimisation of metarules. Machine Learning 2019.

• A. Cropper and S. Tourret: Derivation reduction of metarules in meta-interpretive learning. ILP 2018.

• A. Cropper and S.H. Muggleton: Logical minimisation of meta-rules within meta-interpretive learning. ILP 2014.

For learning dyadic programs without constant symbols, we recommend using these metarules:

metarule([P,Q], [P,A,B], [[Q,A,B]]). % identity
metarule([P,Q], [P,A,B], [[Q,B,A]]). % inverse
metarule([P,Q,R], [P,A,B], [[Q,A],[R,A,B]]). % precon
metarule([P,Q,R], [P,A,B], [[Q,A,B],[R,B]]). % postcon
metarule([P,Q,R], [P,A,B], [[Q,A,C],[R,C,B]]). % chain

Please look at the examples to see how metarules are used.

The above metarules are all non-recursive. By contrast, this metarule is recursive:

metarule([P,Q], [P,A,B], [[Q,A,C],[P,C,B]]).

See the find-duplicate, sorter, member, and string examples.

Metagol supports interpreted background knowledge (IBK), which was initially introduced in these papers:

• A. Cropper, R. Morel, and S.H. Muggleton. Learning higher-order logic programs. Machine Learning 2019.

• A. Cropper and S.H. Muggleton: Learning Higher-Order Logic Programs through Abstraction and Invention. IJCAI 2016.

IBK is usually used to learn higher-order programs. For instance, one can define the map/3 construct as follows:

ibk([map,[],[],_],[]).
ibk([map,[A|As],[B|Bs],F],[[F,A,B],[map,As,Bs,F]]).

Given this IBK, Metagol will try to prove it through meta-interpretation, and will also try to learn a sub-program for the atom [F,A,B].

See the droplast, higher-order, and ibk examples.

You can specify a maximum timeout (in seconds) for Metagol as follows:

metagol:timeout(600). % default 10 minutes

Metagol searches for a hypothesis using iterative deepening on the number of clauses in the solution. You can specify a maximum number of clauses:

metagol:max_clauses(Integer). % default 10

The following flag denotes whether the learned theory should be functional:

metagol:functional. % default false

If the functional flag is enabled, then you must define a func_test predicate. An example func test is:

func_test(Atom1,Atom2,Condition):-
  Atom1 = [P,A,X],
  Atom2 = [P,A,Y],
  Condition = (X=Y).

This func test is used in the robot examples. This test checks that if Metagol can prove any two Atoms Atom1 and Atom2 from an induced program, then the condition X=Y always holds. For more examples of functional tests, see the robots.pl, sorter.pl, and strings2.pl files.

Here are some publications on MIL and Metagol.

• A. Cropper and S. Tourret. Logical minimisation of metarules. Machine Learning 2019.

• A. Cropper, R. Morel, and S.H. Muggleton. Learning higher-order logic programs. Machine Learning 2019.

• S.H. Muggleton, D. Lin, and A. Tamaddoni-Nezhad: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Machine Learning 2015.

• S.H. Muggleton, D. Lin, N. Pahlavi, and A. Tamaddoni-Nezhad: Meta-interpretive learning: application to grammatical inference. Machine Learning 2014.

• A. Cropper and S. Tourret. Logical minimisation of metarules. Machine Learning 2019.

• A. Cropper, R. Morel, and S.H. Muggleton. Learning higher-order logic programs. Machine Learning 2019.

• A. Cropper and S.H. Muggleton: Learning efficient logic programs. Machine learning 2018.

• R. Morel, A. Cropper, and L. Ong. Typed meta-interpretive learning of logic programs. JELIA 2019.

• A. Cropper and S. Tourret: Derivation Reduction of Metarules in Meta-interpretive Learning. ILP 2018.

• A. Cropper and S.H. Muggleton: Learning Higher-Order Logic Programs through Abstraction and Invention. IJCAI 2016.

• A. Cropper and S.H. Muggleton: Learning Efficient Logical Robot Strategies Involving Composable Objects. IJCAI 2015.

• S.H. Muggleton, D. Lin, and A. Tamaddoni-Nezhad: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Machine Learning 2015.

• S.H. Muggleton, D. Lin, N. Pahlavi, and A. Tamaddoni-Nezhad: Meta-interpretive learning: application to grammatical inference. Machine Learning 2014.

• A. Cropper and S.H. Muggleton: Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning. ILP 2014.

• S.H. Muggleton, D. Lin, Jianzhong Chen, and A. Tamaddoni-Nezhad: MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement. ILP 2013.

• A.Cropper: Forgetting to learn logic programs. AAAI 2020.

• A.Cropper: Playgol: learning programs through play. IJCAI 2019.

• S.H. Muggleton, U. Schmid, C. Zeller, A. Tamaddoni-Nezhad, and T.R. Besold: Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP. Machine Learning 2018.

• S. Muggleton, W-Z. Dai, C. Sammut, A. Tamaddoni-Nezhad, J. Wen, and Z-H. Zhou: Meta-Interpretive Learning from noisy images. Machine Learning 2018.

• M. Siebers and U. Schmid: Was the Year 2000 a Leap Year? Step-Wise Narrowing Theories with Metagol. ILP 2018.

• A. Cropper, A. Tamaddoni-Nezhad, and S.H. Muggleton: Meta-Interpretive Learning of Data Transformation Programs. ILP 2015.

• A. Cropper and S.H. Muggleton: Can predicate invention compensate for incomplete background knowledge? SCAI 2015.

• D. Lin, E. Dechter, K. Ellis, J.B. Tenenbaum, and S.H. Muggleton: Bias reformulation for one-shot function induction. ECAI 2014.

• A. Cropper: Efficiently learning efficient programs. Imperial College London, UK 2017

• D. Lin: Logic programs as declarative and procedural bias in inductive logic programming. Imperial College London, UK 2013