  The present work addresses the issue of using complex networks as artificial
intelligence mechanisms. More specifically, we consider the situation in which
puzzles, represented as complex networks of varied types, are to be assembled
by complex network processing engines of diverse structures. The puzzle pieces
are initially distributed on a set of nodes chosen according to different
criteria, including degree and eigenvector centrality. The pieces are then
repeatedly copied to the neighboring nodes. The provision of buffering of
different sizes are also investigated. Several interesting results are
identified, including the fact that BA-based assembling engines tend to provide
the fastest solutions. It is also found that the distribution of pieces
according to the eigenvector centrality almost invariably leads to the best
performance. Another result is that using the buffer sizes proportional to the
degree of the respective nodes tend to improve the performance.
