Abstract
In this paper, we push forward the idea of machine learning
systems for which the operators can be modi ed and netuned 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 aect 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 de ned 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.
