Difference between revisions of "Ryan Hayward"

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* [[Noah Weninger]], [[Ryan Hayward]] ('''2017'''). ''Exploring Positional Linear Go''. [[Advances in Computer Games 15]], [https://webdocs.cs.ualberta.ca/~hayward/papers/lgo.pdf pdf]
 
* [[Noah Weninger]], [[Ryan Hayward]] ('''2017'''). ''Exploring Positional Linear Go''. [[Advances in Computer Games 15]], [https://webdocs.cs.ualberta.ca/~hayward/papers/lgo.pdf pdf]
 
* [[Ryan Hayward]], [[Noah Weninger]] ('''2017'''). ''Hex 2017: MoHex wins the 11x11 and 13x13 tournaments''. [[ICGA Journal#39_34|ICGA Journal, Vol. 39, Nos. 3-4]] » [[20th Computer Olympiad#Hex|20th Computer Olympiad 2017]]
 
* [[Ryan Hayward]], [[Noah Weninger]] ('''2017'''). ''Hex 2017: MoHex wins the 11x11 and 13x13 tournaments''. [[ICGA Journal#39_34|ICGA Journal, Vol. 39, Nos. 3-4]] » [[20th Computer Olympiad#Hex|20th Computer Olympiad 2017]]
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* [[Chao Gao]], [[Martin Müller]], [[Ryan Hayward]] ('''2017'''). ''Focused Depth-first Proof Number Search using Convolutional Neural Networks for the Game of Hex''. [[Conferences#IJCAI2017|IJCAI 2017]]
  
 
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Revision as of 20:20, 1 November 2018

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]

2000 ...

2005 ...

2010 ...

2015 ...

External Links

References

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