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Levente Kocsis,
a Hungarian computer scientiest and researcher in Machine Learning with interests in Reinforcement Learning, Games like Chess, Go, Poker and Lines of Action, Search Control, Neural Networks and optimization algorithms for combinatorial problems. He made his Ph.D thesis Learning Search Decisions [1] in 2003 at the Maastricht University. In 2006, along with Csaba Szepesvári, Levente Kocsis introduced UCT (Upper Confidence bounds applied to Trees), a new algorithm that applies bandit ideas to guide Monte-Carlo planning [2]. Levente Kocsis is member of the Machine Learning Research Group of the Hungarian Academy of Sciences.
Levente Kocsis [3]

Photos

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The Magog team at the 7th Computer Olympiad 2002. Mark Winands, Levente Kocsis, Erik van der Werf [4]

Selected Publications

[5]

2000 ...

2005 ...

2010 ...


External Links


References

  1. ^ Levente Kocsis (2003). Learning Search Decisions. Ph.D thesis, Maastricht University, pdf
  2. ^ Levente Kocsis, Csaba Szepesvári (2006). Bandit based Monte-Carlo Planning
  3. ^ Levente Kocsis | Data Mining and Search Group
  4. ^ MAGOG
  5. ^ ICGA Reference Database(pdf)
  6. ^ Publications - Maastricht University

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