I am assistant professor at the Artificial Intelligence and Integrated Computer Systems division (AIICS). I am working on state space search methods such as decoupled state space
search and have some experience in developing domain-independent
heuristics such as red-black heuristics. More recently, I got interested
in the grounding process that most planning systems perform as
preprocessing. Together with my co-authors, I work on partial grounding
methods that try to tackle the exponential blow-up that can occur during
grounding by leveraging machine learning techniques.
Daniel Gnad
Assistant Professor
My research interests are in the fields of artificial intelligence planning and model checking.
