  Despite significant achievements and current interest in machine learning and
artificial intelligence, the quest for a theory of intelligence, allowing
general and efficient problem solving, has done little progress. This work
tries to contribute in this direction by proposing a novel framework of
intelligence based on three principles. First, the generative and mirroring
nature of learned representations of inputs. Second, a grounded, intrinsically
motivated and iterative process for learning, problem solving and imagination.
Third, an ad hoc tuning of the reasoning mechanism over causal compositional
representations using inhibition rules. Together, those principles create a
systems approach offering interpretability, continuous learning, common sense
and more. This framework is being developed from the following perspectives: as
a general problem solving method, as a human oriented tool and finally, as
model of information processing in the brain.
