Ádám Katona is a research student currently completing a PhD programme with Intelligent Games and Game Intelligence (IGGI) at the University of York.
Ádám did his MSc in mechatronics at Budapest University of Technology and Economics. After graduation, he spent two years working on automated driving at Robert Bosch GmbH, during which he got exposed to both the classical and the machine learning approach of creating intelligent agents.
Adam’s research interests include reinforcement learning, open-ended evolution and genetic encodings for neuroevolution. The aim of his PhD research is to look for ways to automatically discover reward functions that use the internal state of the learning agent. In nature there is no experimenter to define an external reward function, all learning must be driven by internal signals.
Player motivation, Learning, Human-computer interaction, Player modelling, Behaviour analytics