Two-cornered learning classifier systems for pattern generation and classification

Classifying objects and patterns to a certain category is crucial for both humans and machines, so that learnt knowledge may be applied across similar problem instances. Although autonomous learning of patterns by machines has advanced recently, it still requires humans to set up the problem at an appropriate level for the learning technique. If the problem is too complex the system does not learn; conversely, if the problem is too simple the system does not reach its full potential to be able to classify environmental examples. In this work, an automated evolving pattern generator and pattern recognizer has been created for pattern classification problems that can be manipulated autonomously using Learning Classifier Systems (LCSs) at different levels of difficulty. Experiments confirm that both of the agents (e.g. the pattern generation and the pattern classification agent) can be evolved autonomously and co-operatively. The novel contributions in this work enable the effect of domain features on classification performance to become human readable, i.e. possibly determine what features make it difficult for the classification algorithm to learn. This work provides a foundation for a co-evolutionary approach to problem domain creation and the associated learning, such that the agents will trigger evolution when necessary.