A learning model for intelligent agents based on classifier systems and approximate reasoning

The objective of this study is to synthesize a learning model capable of successful and effective operation in hard-to-model environments. We present a structurally simple and functionally flexible model. The model follows the learning patterns experienced by humans. The novelty of the adaptive model lies in the knowledge base, dual learning strategy, and flexible reasoning. The knowledge base is allowed to grow for as long as the agent lives. Learning is brought about by the interaction between two qualitatively different activities leaving long-term and short-term marks on the behavior of the agent. The agent reaches conclusions using approximate reasoning. The focus of the model, the agent, starts life with a blank knowledge base. It learns as it lives. Classifiers are used to represent individual experiences. We demonstrate the functioning of the model through a case study.