David Jian Wu,
an American computer scientist and author of the Arimaa bot Sharp, which won the 2015 Arimaa Challenge and the then $12,000 USD prize by defeating each of three top-ranked human players in a three game series [1]. Sharp already played the 2008 computer tournament, and became runner-up behind David Fotland’s program Bomb, and further won the 2011 and 2014 tournaments but not the contest against the best human players of that time [2]. In 2011, David J. Wu defended his B.Sc. degree at Harvard College, Harvard University, by delivering the thesis Move Ranking and Evaluation in the Game of Arimaa.
Sharp's design was elaborated by its author in the 2015 ICGA Journal, Vol. 38, No. 1[4]. It follows the same fundamental design as strong Chess programs, using an iterative deepeningdepth limited alpha-beta search and various enhancements within a parallel search algorithm conceptually similar to the dynamic tree splitting described by Robert Hyatt in 1994 [5]. Sharp further implements several Arimaa-specific search enhancements with four steps per move, such as static goal detection and capturegeneration, and continues to use and benefit greatly from a move ordering function developed in 2011 as described in Wu's thesis - the move ordering function is the result of training a slightly generalized Bradley-Terry model over thousands of expert Arimaa games to learn to predict expert player's moves, using the same optimization procedure described by Rémi Coulom for computer Go[6].
an American computer scientist and author of the Arimaa bot Sharp, which won the 2015 Arimaa Challenge and the then $12,000 USD prize by defeating each of three top-ranked human players in a three game series [1]. Sharp already played the 2008 computer tournament, and became runner-up behind David Fotland’s program Bomb, and further won the 2011 and 2014 tournaments but not the contest against the best human players of that time [2]. In 2011, David J. Wu defended his B.Sc. degree at Harvard College, Harvard University, by delivering the thesis Move Ranking and Evaluation in the Game of Arimaa.
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Sharp
Sharp's design was elaborated by its author in the 2015 ICGA Journal, Vol. 38, No. 1 [4]. It follows the same fundamental design as strong Chess programs, using an iterative deepening depth limited alpha-beta search and various enhancements within a parallel search algorithm conceptually similar to the dynamic tree splitting described by Robert Hyatt in 1994 [5]. Sharp further implements several Arimaa-specific search enhancements with four steps per move, such as static goal detection and capture generation, and continues to use and benefit greatly from a move ordering function developed in 2011 as described in Wu's thesis - the move ordering function is the result of training a slightly generalized Bradley-Terry model over thousands of expert Arimaa games to learn to predict expert player's moves, using the same optimization procedure described by Rémi Coulom for computer Go [6].Selected Publications
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