MERLIN (Model Extraction by Regular Language INference)
is a non-interactive, multiple predicate learning
system that is particularly suited for learning recursive hypotheses.
it uses an overly general hypothesis in the form of a logic program
together with sets of positive and (possibly) negative examples in order to find an
inductive hypothesis which entails all positive examples but no negative
The basic idea of the approach is to learn finite-state automata that
represent allowed sequences of resolution steps. MERLIN first
finds SLD-refutations for all examples using the overly general hypothesis,
and then tries to find the minimal finite-state automaton that can generate
all sequences of input clauses in the SLD-refutations of the positive
examples and no sequences of input clauses in the SLD-refutations of the
negative examples. After having learned the automaton, MERLIN
produces a new theory that allows only those sequences of resolution steps
which are allowed by both the initial theory and the learned
automaton. This is done by calculating the intersection of the
a grammar that corresponds to the overly general hypothesis. The
intersection is used to derive the final hypothesis in the form of a logic
program. During this process MERLIN may introduce new predicate
symbols, i.e., it carries out a form of predicate invention.
As the induced hypothesis allows only such resolution sequences which are
possible in the initial theory, the initial theory defines MERLIN's
language bias. For instance, as the resolution sequences produced by the
involve only those clauses which are also part of resolution sequences
allowed by the initial theory (besides clauses defining the newly
introduced predicates), it is important that all relevant clauses occur in
the resolution sequences produced by the initial theory.
MERLIN 2.0 provides two modes.
When learning from positive and negative examples, it searches by hill-climbing the minimal
automaton that can generate all positive and no negative sequences.
When learning from positive examples only, MERLIN employs an
incremental hill-climbing technique for inducing the most probable Hidden Markov
Model structure that can generate the positive proof sequences.
In this mode, MERLIN 2.0 completely disregards the negative examples,
e.g. it does not check whether negative examples are covered or not.
Theory-guided induction of logic programs by inference of regular
In L. Saitta, editor, Proceedings of the 13th International
Conference on Machine Learning, pages 46-53. Morgan Kaufmann, 1996.
Predicate invention and learning from positive examples only.
In Proceedings of the Tenth European Conference on Machine
Learning (ECML-98), 1998.