  In this paper, we push forward the idea of machine learning systems whose
operators can be modified and fine-tuned for each problem. This allows us to
propose a learning paradigm where users can write (or adapt) their operators,
according to the problem, data representation and the way the information
should be navigated. To achieve this goal, data instances, background
knowledge, rules, programs and operators are all written in the same functional
language, Erlang. Since changing operators affect how the search space needs to
be explored, heuristics are learnt as a result of a decision process based on
reinforcement learning where each action is defined as a choice of operator and
rule. As a result, the architecture can be seen as a 'system for writing
machine learning systems' or to explore new operators where the policy reuse
(as a kind of transfer learning) is allowed. States and actions are represented
in a Q matrix which is actually a table, from which a supervised model is
learnt. This makes it possible to have a more flexible mapping between old and
new problems, since we work with an abstraction of rules and actions. We
include some examples sharing reuse and the application of the system gErl to
IQ problems. In order to evaluate gErl, we will test it against some structured
problems: a selection of IQ test tasks and some experiments on some structured
prediction problems (list patterns).

Machine Learning