Classifiers are one of the most widely applied approaches in Artificial
Intelligence (AI). However, the employment of classifiers in critical
applications would render any errors in these systems more consequential;
particularly due to the lack of formal verification methods in these systems.
This study aims to develop a verification method that eliminates errors through
the integration of multiple classifiers. In order to do this, primarily, we
have defined a special property for the classifiers which extracts the
knowledge of these classifiers. Secondly, we have designed a multi-agent
system, comprised of multiple classifiers, in order to check the satisfaction
of the aforementioned special property. Also, in order to help examine the
reasoning concerning the aggregation of the distributed knowledge, itself
gained through the combined effort of separate classifiers and acquired
external information sources, a dynamic epistemic logic-based method has been
proposed. Our proposed model is capable of verifying itself given specific
inputs if the cumulative knowledge of the entire system proves their
correctness, which results in self-awareness of this system. Finally, we
applied this model to the MNIST dataset, and it successfully reduced the error
rate to approximately one-tenth of the individual classifiers. In conclusion,
we have formulated and developed a Multi-Agent Systems' Knowledge-Sharing
algorithm (MASKS) and verified its utility compared to individual classifiers
using the MNIST dataset.Comment: 17 pages, 8 figure