Cognitive learning using evolutionary computing

Artificial Cognitive Systems encompasses machine intelligence systems, such as robots, that interact with their environment. This talk will highlight research that enables such systems to learn and adapt to problems in their domain and in related domains. The symbolic evolutionary computation technique of Learning Classifier Systems (LCSs) was conceived 40 years ago as an artificial cognitive system. The work presented shows how LCSs can utilise building blocks of knowledge in heuristics ('if-then' rules) to transfer learnt knowledge from small to large scale problems in the same domain. Furthermore, the use of these rules enables functionality learned in sub-problems to be transferred to related problems. Results show that provided the human experimenter can set a rough curriculum for learning concepts, the underlying patterns/models in a problem domain can be learnt in an interpretable manner.