  In this work we propose a computational scheme inspired by the workings of
human cognition. We embed some fundamental aspects of the human cognitive
system into this scheme in order to obtain a minimization of computational
resources and the evolution of a dynamic knowledge network over time, and apply
it to computer networks. Such algorithm is capable of generating suitable
strategies to explore huge graphs like the Internet that are too large and too
dynamic to be ever perfectly known. The developed algorithm equips each node
with a local information about possible hubs which are present in its
environment. Such information can be used by a node to change its connections
whenever its fitness is not satisfying some given requirements. Eventually, we
compare our algorithm with a randomized approach within an ecological scenario
for the ICT domain, where a network of nodes carries a certain set of objects,
and each node retrieves a subset at a certain time, constrained with limited
resources in terms of energy and bandwidth. We show that a cognitive-inspired
approach improves the overall networks topology better than a randomized
algorithm.
