Dr. Lukas Chrpa

Research Fellow
PARK research group
University of Huddersfield
l.chrpa [AT] hud.ac.uk

Passionate about making machines think to improve quality of humans everyday life.

Research Interests

DBLP Profile
Google Scholar Profile

Key facts

Since 11/2011, Dr. Lukas Chrpa is a research fellow at PARK (Planning, Autonomy and Representation of Knowledge) research group, University of Huddersfield. His research concerns applying Machine Learning and Knowledge Engineering techniques in AI planning. In May 2012 - Sep 2016, he was a key participant on a project entitled "Machine Learning and Adaptation of Domain Models to Support Real Time Planning in Autonomous Systems", funded by EPSRC under the "Autonomous Intelligent Systems" call. The project is being done in collaboration with University of Edinburgh and Schlumberger. The project aims on designing, modelling and learning planning domain models for Oil-drilling industry.

He was a co-organizer of 8th International Planning Competition (IPC-2014) - Deterministic track.

He is co-organizing the 5th International Competition on Knowledge Engineering for Planning and Scheduling.

In 2005, Dr. Lukas Chrpa obtained master degree (equivalent to MSc.) in Computer Science at Palacky University in Olomouc. After that he moved to Charles University in Prague for a PhD study. Main areas for his PhD studies were related to automated planning, machine learning, linear logic, and logic programming (see his old website). His supervisor was Prof. Roman Bartak who is a respected scientist in Artificial Intelligence, especially automated planning, scheduling and constraint satisfaction. In September 2009 he successfully defended his thesis entitled "Learning for Classical Planning". After that he spent almost 2 years as a postdoc researcher at Agent Technology Center at Czech Technical University in Prague where he performed application oriented research in path (trajectory) planning and multi-agent simulation.


PTT (Planning Task Transformer) is a toolkit for reformulating planning tasks by learning macro-operators and/or entanglements. In particular: Download sources here
Download examples here