Using an induced relational decision tree for rule injection in a learning classifier system

Transfer learning, using systems with rich and general representations, to improve adaptive rule based systems designed to efficiently react in changing environments is the idea behind the problem studied in this paper. In this framework, the aim of this research is studying the benefits of using relational learning in combination with an evolutionary propositional learning system as XCS. The proposed method starts by learning a first order relational decission tree using a set of simplified instances of a problem. The learned relational model is then used to help a learning classifier system to deal with a more complex instance of the task. The researched strategy is based on injecting rules derived from the relational model in the discovering subsystem of the XCS. Results show that this method can be used to automatically adapt the behaviour of a learning rule based system when the environment increases its complexity.