This paper explains our need for adaptive systems and outlines the conceptual model of a knowledge-based system that adapts through learning. The proposed model utilizes multiple human learning processes instead of ad hoc mathematical techniques to manifest its learning behavior. Static systems have proven to be very brittle or short-lived when faced with changing demands of their environment.
Systems can adapt either through learning or through evolution.
In terms of time, learning mechanisms responsible for adaptation are more efficient when compared to slow evolutionary processes. The purpose of this research is to show that it is feasible to utilize human learning processes as a basis for designing multistrategy machine learning systems capable of exhibiting adaptive behavior in dynamic environments. Conclusion A. An adaptive computer-based system should be a learning system that interacts with its environment and gains functional integration with its environment through acquisition, modification, and discovery of knowledge. This is because of the technological limitations involved in implementing motor and sensory subsystems used by human beings to interact with their environment. This is especially true for human learning types one through four.