Browsing by Author "Wu, Hung-Chun"

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A New MCTS-Based Algorithm for Multi-Objective Flexible Job Shop Scheduling Problem
(2015)
Chou, Jen-Jai
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Liang, Chao-Chin
;
Wu, Hung-Chun
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Wu, I-Chen
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Wu, Tung-Ying
Multi-objective flexible job-shop scheduling problem (MO-FJSP) is very important in both fields of production management and combinatorial optimization. Wu et al. proposed a Monte-Carlo Tree Search (MCTS) to solve MO-FJSP and successfully improved the performance of MCTS to find 17 Pareto solutions: 4 of Kacem 4x5, 3 of 10x7, 4 of 8x8, 4 of 10x10, and 2 of 15x10. This paper proposes a new MCTS-based algorithm for MO-FJSP problem by modifying their algorithm. Our experimental results show that our new algorithm significantly outperforms their algorithm for large problems, especially for Kacem 15x10. This shows that the new algorithm tends to have better potential of solving harder MO-FJSP problems.
Multilabeled Value Networks for Computer Go
(2018-12-01)
Wu, Ti-Rong
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Wu, I-Chen
;
Chen, Guan-Wun
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Wei, Ting-Han
;
Wu, Hung-Chun
This paper proposes a new approach to a novel value network architecture for the game Go, called a multilabeled (ML) value network. In the ML value network, different values (win rates) are trained simultaneously for different settings of komi, a compensation given to balance the initiative of playing first. The ML value network has three advantages: 1) it outputs values for different komi; (2) it supports dynamic komi; and (3) it lowers the mean squared error (MSE). This paper also proposes a new dynamic komi method to improve game-playing strength. This paper also performs experiments to demonstrate the merits of the architecture. First, the MSE of the ML value network is generally lower than the value network alone. Second, the program based on the ML value network wins by a rate of 67.6% against the program based on the value network alone. Third, the program with the proposed dynamic komi method significantly improves the playing strength over the baseline that does not use dynamic komi, especially for handicap games. To our knowledge, up to date, no handicap games have been played openly by programs using value networks. This paper provides these programs with a useful approach to playing handicap games.