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Arkadiusz Nowakowski,
a Polish computer scientist and researcher at Institute of Computer Science, University of Silesia, Sosnowiec. His research interests in computational intelligence include combinatorial game theory and search algorithms. In 2013, Arkadiusz Nowakowski applied Monte-Carlo Tree Search with UCT (Upper Confidence bounds applied to Trees) to the Partisan game Toads and Frogs.

Toads and Frogs

Along with Wojciech Wieczorek, Rafał Skinderowicz, Jan Kozak, and Przemysław Juszczuk et al., Arkadiusz Nowakowski participated the Second Silesian University Computer Combinatorial Games Tournament in Sosnowiec, on June 17, 2013, playing Toads and Frogs, an abstract, two-player, strategy game invented by Richard Kenneth Guy [1]. Bots played a Blitz tournament with a board size of 32 squares, two seconds per move, the games to be played automatically using a specially defined IP-based protocol. Following algorithms were used: Monte-Carlo Tree Search [2] (Wieczorek), iterative deepening alpha-beta (Skinderowicz), building patterns and decision rules [3] (Kozak) , and UCT [4] (Nowakowski), as descibed in the December 2013 ICGA Journal with following conclusion [5]:
In this article, we described four algorithms for playing a game quickly. The originality of two of them relies on the uncommon application of well-known methods. In the first algorithm, parallel Monte-Carlo tree search with the help of a fixed array has been proposed. In the second algorithm, the patterns and rules approach has been applied. The remaining two algorithms, which have been given as an instance of common methods for comparison purposes, are the iterative deepening version of alpha-beta game tree search and plain UCT. On this foundation, four computer programs were prepared and submitted to a computer Toads-and-Frogs blitz tournament at the Silesian University. Our modification of MCTS undoubtedly had the best performance in the tournament and in additional experiments (all-play-all mini-tournaments).

Selected Publications

External Links


  1. ^ Elwyn Berlekamp, John H. Conway, Richard K. Guy (1982). Winning Ways for your Mathematical Plays. 1st edition, Academic Press
  2. ^ Cameron Browne, et al. (2012). A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games, Volume 4, pdf
  3. ^ Ross Quinlan (1979). Discovering Rules by Induction from Large Collections of Examples. Expert Systems in the Micro-electronic Age, pp. 168-201. Edinburgh University Press
  4. ^ Levente Kocsis, Csaba Szepesvári (2006). Bandit based Monte-Carlo Planning. ECML-06, LNCS/LNAI 4212
  5. ^ Wojciech Wieczorek, Rafał Skinderowicz, Jan Kozak, Przemysław Juszczuk, Arkadiusz Nowakowski (2013). Selected Algorithms from the 2013 Toads-and-Frogs Blitz Tournament. ICGA Journal, Vol. 36, No. 4

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