Abstract: AI-guided scientific discovery will become an indispensable tool for scientists in the not too distant future. This tutorial begins by introducing the area of computational scientific discovery within the cognitive sciences along with an overview of recent models of cognitive behaviour developed using these tools. The second half of the tutorial presents a detailed walk-through of our methodology and computational system, where participants will learn how to design experiments, evolve and analyse candidate models using the GEMS (Genetically Evolving Models in Science) system. Although specialised towards the cognitive sciences, many of the principles of model definition and discovery can be more broadly applied, and so the tutorial should be of interest to the wider Cognitive Machine Intelligence community.

Bio: Peter Lane has degrees in Mathematics and Computer Science. His research interests cover many aspects of machine learning and its applications, including the simulation of human learning. His early work looked at connectionist models of natural-language learning. More recently, he has worked on the CHREST cognitive architecture of human perception and learning. His current project uses genetic programming to develop cognitive models from psychological data.