MASKS: A Multi-Classifier's verification approach

Abstract

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