  In this paper, we synthesize a data-driven method to predict the optimal
topology of an ad-hoc robot network. This problem is technically a multi-task
classification problem. However, we divide it into a class of multi-class
classification problems that can be more efficiently solved. For this purpose,
we first compose an algorithm to create ground-truth optimal topologies
associated with various configurations of a robot network. This algorithm
incorporates a complex collection of optimality criteria that our learning
model successfully manages to learn. This model is an stacked ensemble whose
output is the topology prediction for a particular robot. Each stacked ensemble
instance constitutes three low-level estimators whose outputs will be
aggregated by a high-level boosting blender. Applying our model to a network of
10 robots displays over 80% accuracy in the prediction of optimal topologies
corresponding to various configurations of the cited network.
