Over the last few years, machine learning and data mining methods (MLDM) are constantly evolving, in order to accelerate the process of knowledge discovery from data (KDD). Today's challenge is to select only the most relevant knowledge from those extracted. The present paper is directed to these purposes, by developing a new concept of knowledge mining for meta-knowledge extraction, and extending the most popular machine learning methods to extract meta-models. This new concept of knowledge classification is integrated on the cognitive agent architecture, so as to speed-up its inference process. With this new architecture, the agent will be able to select only the actionable rule class, instead of trying to infer its whole rule base exhaustively.