Abstract
Artificial Neural networks are one of the most widely applied approaches
for classification problems. However, developing an errorless artificial
neural network is in practice impossible, due to the statistical nature
of such networks. The employment of artificial neural networks in
critical applications has rendered any such emerging errors, in these
systems, incredibly more significant. Nevertheless, the real
consequences of such errors have not been addressed, especially due to
lacking verification approaches. This study aims to develop a
verification method that eliminates errors through the integration of
multiple artificial neural networks. In order to do this, first of all,
a special property has been defined, by the authors, to extract the
knowledge of these artificial neural networks.
Furthermore, a multi-agent system has been designed, itself comprised of
multiple artificial neural networks, in order to check whether the
aforementioned special property has been satisfied, or not. Also, in
order to help examine the reasoning concerning the aggregation of the
distributed knowledge, itself gained through the combined effort of
separate artificial neural networks and acquired external information
sources, a dynamic epistemic logic-based method has been proposed.
Finally, we believe aggregated knowledge may lead to self-awareness for
the system. As a result, our model shall be capable of verifying
specific inputs, if the cumulative knowledge of the entire system proves
its correctness.
In conclusion, and formulated for multi-agent systems, a
knowledge-sharing algorithm (Abbr. MASKS) has been developed. Which
after being applied on the MNIST dataset successfully reduced the error
rate to roughly one-eighth of previous runs on individual artificial
neural network in the same model.