  Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems
of local cooperation between agents. We present smapy, an ensemble based AMAS
implementation for mobility prediction, whose agents are provided with machine
learning models in addition to their cooperation rules. With a detailed
methodology, we propose a framework to transform a classification problem into
a cooperative tiling of the input variable space. We show that it is possible
to use linear classifiers for online non-linear classification on three
benchmark toy problems chosen for their different levels of linear
separability, if they are integrated in a cooperative Multi-Agent structure.
The results obtained show a significant improvement of the performance of
linear classifiers in non-linear contexts in terms of classification accuracy
and decision boundaries, thanks to the cooperative approach.
