Abstract
In today’s world of excessive development in technologies, sustainability and adaptability of computer applications is a challenge, and future prediction became significant. Therefore, strong artificial intelligence (AI) became important and, thus, statistical machine learning (ML) methods were applied to serve it. These methods are very difficult to understand, and they predict the future without showing how. However, understanding of how machines make their decision is also important, especially in information system domain. Consequently, incremental covering algorithms (CA) can be used to produce simple rules to make difficult decisions. Nevertheless, even though using simple CA as the base of strong AI agent would be a novel idea but doing so with the methods available in CA is not possible. It was found that having to accurately update the discovered rules based on new information in CA is a challenge and needs extra attention. In specific, incomplete data with missing classes is inappropriately considered, whereby the speed and data size was also a concern, and future none existing classes were neglected. Consequently, this paper will introduce a novel algorithm called RULES-IT, in order to solve the problems of incremental CA and introduce it into strong AI. This algorithm is the first incremental algorithm in its family, and CA as a whole, that transfer rules of different domains to improve the performance, generalize the induction, take advantage of past experience in different domain, and make the learner more intelligent. It is also the first to introduce intelligent aspects into incremental CA, including consciousness, subjective emotions, awareness, and adjustment. Furthermore, all decisions made can be understood due to the simple representation of repository as rules. Finally, RULES-IT performance will be benchmarked with six different methods and compared with its predecessors to see the effect of transferring rules in the learning process, and to prove how RULES-IT actually solved the shortcoming of current incremental CA in addition to its improvement in the total performance.