Difference between revisions of "Ryan Hayward"

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* [[Chao Gao]], [[Siqi Yan]], [[Ryan Hayward]], [[Martin Müller]] ('''2018'''). ''A transferable neural network for Hex''. [[CG 2018]], [[ICGA Journal#40_3|ICGA Journal, Vol. 40, No. 3]]
 
* [[Chao Gao]], [[Siqi Yan]], [[Ryan Hayward]], [[Martin Müller]] ('''2018'''). ''A transferable neural network for Hex''. [[CG 2018]], [[ICGA Journal#40_3|ICGA Journal, Vol. 40, No. 3]]
 
* [[Chao Gao]], [[Kei Takada]], [[Ryan Hayward]] ('''2019'''). ''Hex 2018: MoHex3HNN over DeepEzo''. [[ICGA Journal#41_1|ICGA Journal, Vol. 41, No. 1]] » [[21st Computer Olympiad#Hex|21st Computer Olympiad 2018]]
 
* [[Chao Gao]], [[Kei Takada]], [[Ryan Hayward]] ('''2019'''). ''Hex 2018: MoHex3HNN over DeepEzo''. [[ICGA Journal#41_1|ICGA Journal, Vol. 41, No. 1]] » [[21st Computer Olympiad#Hex|21st Computer Olympiad 2018]]
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* [[Nicolas Fabiano]], [[Ryan Hayward]] ('''2019'''). ''New Hex Patterns for Fill and Prune''. [[Advances in Computer Games 16]]
 
==2020 ...==
 
==2020 ...==
 
* [[Ryan Hayward]], et al. ('''2021'''). ''BoxOff is NP-complete''. [[Advances in Computer Games 17]]
 
* [[Ryan Hayward]], et al. ('''2021'''). ''BoxOff is NP-complete''. [[Advances in Computer Games 17]]

Latest revision as of 16:25, 1 December 2021

Home * People * Ryan Hayward

Ryan B. Hayward [1]

Ryan Bruce Hayward,
a Canadian mathematician, computer scientist, and professor at Department of Computing Science at University of Alberta. Ryan Hayward is particularly interested in Hex, which he learned from Claude Berge. As member of the University of Alberta's GAMES research group [2], he leads a team that developed Hex solver and players.

Hex Programs

After early trials with Mongoose, the Hex programs Wolve (2008) and MoHex (2009, 2010, 2011, 2013, 2015 and 2017) won Gold Medals in Hex at the Computer Olympiad.

Wolve

Wolve does a truncated Alpha-Beta search of two and up to four plies, considering the huge Branching Factor of Hex.

MoHex

Since 2009 Monte-Carlo Tree Search starts to dominate, and MoHex applies MCTS along with the UCT framework combined with the allmoves-as-first (AMAF) heuristic to select the best child during tree traversal [3].

MoHex-CNN

MoHex-CNN, which won the 13x13 competition of the 20th Computer Olympiad 2017 is a convolutional neural net (CNN) version of MoHex. At each new node of the Monte-Carlo search tree, a policy CNN biases child selection by initializing child visit and win counts with artificial values [4].

Selected Publications

[5]

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2005 ...

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External Links

References

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