Using biofunctionality as a novel learning method for intelligent agents

A learning machine envisioned for functioning in the physical world should be reasonably easy to implement. 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. The 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 this case study how a fuzzy logic controller helps in making the system more conforming to the biofunctional model. The result is vast improvements to the learning rate and the overall efficiency of the whole system.