Johannes Fürnkranz

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Johannes Fürnkranz, an Austrian mathematician, computer scientist, and AI researcher, since 2004 Professor in knowledge engineering at the TU Darmstadt. He holds a Ph.D. in 1997 from Vienna University of Technology under advisor Gerhard Widmer. His research spans various AI-fields, such as machine learning, data mining, and more recently Monte-Carlo tree search. He is editorial board member of various scientific AI-Periodical, such as the Journal of Artificial Intelligence Research (JAIR), and is editor-in-chief of the bimonthly Data Mining and Knowledge Discovery journal.

=Selected Publications=

1996 ...

 * Johannes Fürnkranz (1996). Machine Learning in Computer Chess: The Next Generation. ICCA Journal, Vol. 19, No. 3, zipped ps
 * Johannes Fürnkranz (1997). Knowledge Discovery in Chess Databases: A Research Proposal. Technical Report OEFAI-TR-97-33, Austrian Research Institute for Artificial Intelligence, pdf
 * Johannes Fürnkranz (1997). Pruning Algorithms for Rule Learning. Machine Learning, Vol. 27, pdf
 * Johannes Fürnkranz (1997). Efficient Pruning Methods for Relational Learning. Ph.D. thesis, TU Vienna, advisor Gerhard Widmer
 * Franz-Günter Winkler, Johannes Fürnkranz (1997). On Effort in AI Research: A Description Along Two Dimensions. Deep Blue versus Kasparov, AAAI Workshop
 * Franz-Günter Winkler, Johannes Fürnkranz (1998). A Hypothesis on the Divergence of AI Research. ICCA Journal, Vol. 21, No. 1, pdf
 * Johannes Fürnkranz, Bernhard Pfahringer (1998). Guest Editorial: First-Order Knowledge Discovery in Databases. Applied Artificial Intelligence, Vol. 12, No. 5
 * Johannes Fürnkranz (1998). Integrative Windowing. arXiv:cs/9805101

2000 ...

 * Johannes Fürnkranz (2000). Machine Learning in Games: A Survey. Austrian Research Institute for Artificial Intelligence, OEFAI-TR-2000-3, pdf
 * Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer (2000). Learning to Use Operational Advice. ECAI-00, pdf
 * Johannes Fürnkranz, Miroslav Kubat (eds.) (2001). Machines that Learn to Play Games. Advances in Computation: Theory and Practice, Vol. 8,. NOVA Science Publishers
 * Michael Bowling, Johannes Fürnkranz, Thore Graepel, Ron Musick (2006). Machine learning and Games. Machine Learning, Vol. 63, No. 3
 * Johannes Fürnkranz (2007). Recent advances in machine learning and game playing. ÖGAI Journal, Vol. 26, No. 2, Computer Game Playing, pdf
 * Sacha Droste, Johannes Fürnkranz (2008). Learning of Piece Values for Chess Variants. Technical Report TUD–KE–2008-07, Knowledge Engineering Group, TU Darmstadt, pdf
 * Sacha Droste, Johannes Fürnkranz (2008). Learning the Piece Values for three Chess Variants. ICGA Journal, Vol. 31, No. 4

2010 ...

 * Johannes Fürnkranz, Eyke Hüllermeier (eds.) (2010). Preference Learning. Springer
 * Christian Wirth, Johannes Fürnkranz (2015). On Learning From Game Annotations. IEEE Transactions on Computational Intelligence and AI in Games, Vol. 7, No. 3
 * Johannes Fürnkranz (2017). Machine Learning and Game Playing. in Claude Sammut, Geoffrey I. Webb (eds) (2017). Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA
 * Tobias Joppen, Miriam Moneke, Nils Schroder, Christian Wirth, Johannes Fürnkranz (2018). Informed Hybrid Game Tree Search for General Video Game Playing. IEEE Transactions on Computational Intelligence and AI in Games, Vol. 10, No. 1
 * Tobias Joppen, Christian Wirth, Johannes Fürnkranz (2018). Preference-Based Monte Carlo Tree Search. arXiv:1807.06286
 * Tobias Joppen, Johannes Fürnkranz (2019). Ordinal Monte Carlo Tree Search. TU Darmstadt, arXiv:1901.04274

=Forum Posts=
 * Re: Computer Chess and alpha-beta pruning by Johannes Fürnkranz, rgc, September 22, 1993 » Alpha-Beta, Iterative Deepening
 * ChessBase for Windows and/or Fritz 3 Review by Johannes Fürnkranz, rgc, September 06, 1994 » Fritz
 * ChessBase for Windows by Johannes Fürnkranz, rgc, November 01, 1994 » ChessBase (Database)

=External Links=
 * Johannes Fürnkranz - Knowledge Engineering Group
 * Johannes Fürnkranz - Google Scholar Citations

=References= Up one level