Julian Oleg Arenz, a German computer scientist and Ph.D. student at the Computational Learning for Autonomous Systems Labrotary at Darmstadt University of Technology. His research interests include machine learning and robotics, in particular imitation learning, inverse reinforcement learning and hierachical learning. His 2012 Bachelor's thesis Monte Carlo Chess covers Monte-Carlo Tree Search in the game of Chess. His B.Sc. thesis chess engine MCC, based on Stockfish, applies UCT instead of alpha-beta. Despite improvements mainly by modifications that increase the accuracy of the simulation strategy of more than 850 Elo over the base implementation, MCC performed still too bad to compete with minimax based chess programs [1], also mentioning the shallow trap property of Chess [2] as one possible cause.
Oleg Arenz [3]

Selected Publications

• Oleg Arenz, Sarah Magin (2011). Implementation of signature schemes with additional properties in C and Java. Cryptography Lab Project, Darmstadt University of Technology, pdf hosted by Pierre-Louis Cayrel
• Oleg Arenz (2012). Monte Carlo Chess. B.Sc. thesis, Darmstadt University of Technology, advisor Johannes Fürnkranz, pdf
• Oleg Arenz (2014). Feature Extraction for Inverse Reinforcement Learning. Master thesis, Darmstadt University of Technology, pdf

External Links

• Oleg Arenz - Autonomous Systems Labs: CLAS and IAS from Darmstadt University of Technology
• Oleg Arenz - Google Scholar Citations
• Julian Oleg Arenz - SC Landskrone - Schach

References

1. ^ Oleg Arenz (2012). Monte Carlo Chess. B.Sc. thesis, Darmstadt University of Technology
2. ^ Raghuram Ramanujan, Ashish Sabharwal, Bart Selman (2010). On Adversarial Search Spaces and Sampling-Based Planning. ICAPS 2010
3. ^ Oleg Arenz - Autonomous Systems Labs: CLAS and IAS from Darmstadt University of Technology
4. ^ Oleg Arenz - Knowledge Engineering Publications

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