Applying classifier systems to learn the reactions in mobile robots

The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier Systems (CS) have proven their ability of continuos learning, however they have some problems in reactive systems. A modified CS, Reactive Classifier System (RCS), is proposed to overcome those problems. Two special mechanisms are included in RCS: the non-existence of internal cycles inside the CS (No Internal Cycles) and the fusion of environmental message with the messages posted to message list in previous instant (Generation List Through Fusion). These mechanisms allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminating between rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations.