A novel learning method for intelligent agents using biofunctionality

Building a knowledge base for an intelligent system is the main goal in the development of any learning machine. Our daily lives and experiences suggest that human-like learning systems are better suited for functioning in hard-to-navigate environments because of their high degree of flexibility. This paper applies the biofunctional model of human learning to the design and implementation of a learning machine that is effective in navigating complex environments and relatively easy to design using classifier systems. We portray in the case study how a fuzzy logic controller links the system to the biofunctional model. It also makes vast improvements to the learning rate and the overall efficiency of the whole system