S
Shi-Jim Yen
Researcher at National Dong Hwa University
Publications - 98
Citations - 694
Shi-Jim Yen is an academic researcher from National Dong Hwa University. The author has contributed to research in topics: Computer Go & Tournament. The author has an hindex of 13, co-authored 95 publications receiving 628 citations. Previous affiliations of Shi-Jim Yen include National University of Tainan.
Papers
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Current Frontiers in Computer Go
TL;DR: It is seen that in 9 × 9, computers are very close to the best human level, and can be improved easily for the opening book; whereas in 19 × 19, handicap 7 is not enough for the computers to win against top level professional players, due to some clearly understood weaknesses of the current algorithms.
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Computer chinese chess
TL;DR: The article introduces some techniques for developing Chinese-chess programs and assumes that after DEEP BLUE’s victory over Kasparov in 1997, Chinese chess will be the next popular chess-like board game at which a program will defeat a human top player.
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Two-Stage Monte Carlo Tree Search for Connect6
Shi-Jim Yen,Jung-Kuei Yang +1 more
TL;DR: Two-stage MCTS is proposed, which combines normal TSS with conservative threat space search (CTSS) and aims at finding the most promising move of the initial position and is considerably more efficient than traditional MCTs on those positions with TSS solution in Connect6.
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Human vs. Computer Go: Review and Prospect [Discussion Forum]
Chang-Shing Lee,Mei-Hui Wang,Shi-Jim Yen,Ting-Han Wei,I-Chen Wu,Ping-Chiang Chou,Chun-Hsun Chou,Ming-Wan Wang,Tai-Hsiung Yan +8 more
TL;DR: In this paper, the development of computational intelligence and its relative strength in comparison with human intelligence for the game of Go is discussed. And the Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development.
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T2FS-Based Adaptive Linguistic Assessment System for Semantic Analysis and Human Performance Evaluation on Game of Go
TL;DR: This paper uses type-2 fuzzy sets with parameters optimized by a genetic algorithm for estimating the rank in a stable manner, independently of board size, and uses an adaptive Monte Carlo tree search (MCTS) to estimate the number of simulations corresponding to the strength of its opponents.