Séminaire NEURO : Will Browne

Titre du séminaire et orateur

Cognitive Learning using Evolutionary Computation.

Will Browne, Associate Professor, School of Engineering and Computer Science, Victoria University of Wellington, New Zealand.

Date et lieu

Lundi 14 novembre 2016, 10h30.

Université de Cergy-Pontoise, St-Martin 1, bât A, 5ème étage, salle 570.


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.


Will Browne received a BEng Mechanical Engineering, Honours degree from the University of Bath, UK in 1993, MSc in Energy (1994) and EngD (Engineering Doctorate scheme, 1999) University of Wales, Cardiff. After eight years lecturing in the Department of Cybernetics, University of Reading, UK, he was appointed to School of Engineering and Computer Science, Victoria University of Wellington, NZ in 2008. Associate Professor Browne's main area of research is Applied Cognitive Systems. This includes Learning Classifier Systems, Cognitive Robotics, and Modern Heuristics for industrial application. Blue skies research includes analogues of emotions, abstraction, memories, dissonance and machine consciousness.  He is an Associate Editor for Neural Computing and Applications, and Applied Soft Computing. He has published over 100 academic papers, including in IEEE Transactions on Evolutionary Computation on scalable learning and two best paper awards in Genetic and Evolutionary Computation Conference.