Soft Computing for Agent-Based Decision Making Using the Biofunctional Theory of Knowledge
Abdollah Homaifar, H. Hawari, C.W. Bou-Saba, Albert Esterline, Asghar Iran‐Nejad, Eddie Tunstel · 2006
This paper applies the biofunctional model of human learning to the implementation of a learning machine that is effective in navigating complex environments. The target model is rule-based and is highly flexible in establishing the relation between any state-action pair. The learning machine is designed using X classifier systems and a fuzzy logic controller (FLC). A learning machine is built in simulation that closely approximates the learning characteristics of the human brain as described by the theory of biofunctional cognition. The methodology is tested with experiments using both single and multiple agents. We also investigated the effectiveness of biofunctionality using competitive and cooperative modes. Furthermore, we studied the robustness of our approach. Our results show that the integration of a FLC and an X classifier system, realizing a biofunctional model, provides a methodology for constructing very effective learning machines.