Computer shogi
By H. Iida, M. Sakuta, Jeff Rollason. 2002
62 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Developmental and Educational Psychology, Sociology and Political Science, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Pieter Abbeel
- Yuxi Li
- Yoshua Bengio
- Timothy P. Lillicrap
- David Silver
- Chelsea Finn
- Ruslan Salakhutdinov
In Developmental and Educational Psychology
- Timothy P. Lillicrap
- Demis Hassabis
- Dharshan Kumaran
- Thore Graepel
- David Silver
- Kunihito Hoki
- Tomoyuki Kaneko
- Yuxi Li
In Sociology and Political Science
- Arthur Guez
- David Silver
- Thore Graepel
- Marc Lanctot
- Matthew Lai
- Kunihito Hoki
- Tomoyuki Kaneko
- R´emi Munos
In Reinforcement Learning
- Sergey Levine
- Pieter Abbeel
- Timothy P. Lillicrap
- Yuxi Li
- David Silver
- Jimmy Ba
- Shimon Whiteson
- R´emi Munos
Citations in the corpus
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017, background): “-champion Stockfish (25); other strong chess programs, including Deep Blue, use very similar architectures (9,21). Shogi is a significantly harder game, in terms of computational complexity, than chess (2, 14): it is played on a larger board, and any captured opponent piece changes sides and may subse”
- Efficiency of three forward-pruning techniques in shogi: Futility pruning, null-move pruning, and Late Move Reduction (LMR) (2012, background): “The second difference is the limited mobility of shogi pieces [23].”
- Analysis of Evaluation-Function Learning by Comparison of Sibling Nodes (2011, methodology): “The experiments were conducted on a computer program for playing shogi, a Japanese variant of chess [11].”
- Large-Scale Optimization for Evaluation Functions with Minimax Search (2014, background): “There are 13 types of pieces in shogi (Iida et al., 2002).”