Browsing by Author "Liang, Chao-Chin"

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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
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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.
JOB-LEVEL ALGORITHMS FOR CONNECT6 OPENING BOOK CONSTRUCTION
(2015-09-01)
Wei, Ting Han
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Wu, I-Chen
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Liang, Chao-Chin
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Chiang, Bing-Tsung
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Tseng, Wen-Jie
This article investigates the use of job-level (JL) algorithms for Connect6 opening book construction. In the past, JL proof-number search (JL-PNS) was successfully used to solve Connect6 positions. Using JL-PNS, many opening plays that lead to losses can be eliminated from consideration during the opening game. However, it is unclear how the information of unsolved positions can be exploited for opening book construction. For this issue, the current article first proposes four heuristic metrics when using JL-PNS to estimate move quality. The article then proposes a JL upper confidence tree (JL-UCT) algorithm and three heuristic metrics that work with JL-UCT. Of the three, the best way to estimate move quality for JL-UCT is the number of nodes in each candidate move's subtree. In order to compare the heuristic metrics among the two algorithms objectively, we proposed two kinds of measurement methods to analyze the suitability of these metrics when choosing best moves for a set of benchmark positions. Experimental results show that the node count heuristic metric for JL-UCT outperforms all other heuristic metrics, including the four for JL-PNS. We then verify the results by constructing three separate opening books using the top three performing heuristic metrics. Competitive play also shows that the node count heuristic metric for JL-UCT is most suitable among the currently proposed heuristic metrics for Connect6 opening book construction.
Job-Level Algorithms for Connect6 Opening Position Analysis
(2014-01-01)
Wei, Ting-Han
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Wu, I-Chen
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Liang, Chao-Chin
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Chiang, Bing-Tsung
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Tseng, Wen-Jie
This paper investigates job-level (JL) algorithms to analyze opening positions for Connect6. The opening position analysis is intended for opening book construction, which is not covered by this paper. In the past, JL proof-number search (JL-PNS) was successfully used to solve Connect6 positions. Using JL-PNS, many opening plays that lead to losses can be eliminated from consideration during the opening game. However, it is unclear how the information of unsolved positions can be exploited for opening book construction. For this issue, this paper first proposes four heuristic metrics when using JL-PNS to estimate move quality. This paper then proposes a JL upper confidence tree (JL-UCT) algorithm and some heuristic metrics, one of which is the number of nodes in each candidate move\'s subtree. In order to compare these metrics objectively, we proposed two kinds of measurement methods to analyze the suitability of these metrics when choosing best moves for a set of benchmark positions. The results show that for both metrics this node count heuristic metric for JL-UCT outperforms all the others, including the four for JL-PNS.
Multi-Objective Flexible Job Shop Scheduling Problem Based on Monte-Carlo Tree Search
(2013-01-01)
Wu, Tung-Ying
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Wu, I-Chen
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Liang, Chao-Chin
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資訊工程學系
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Department of Computer Science
Flexible job-shop scheduling problem (FJSP) is very important in both fields of production management and combinatorial optimization. This paper focuses on the multi-objective flexible job shop scheduling problem (MO-FJSP) with three objectives which minimizing makespan, total workload and maximal workload, respectively, with Pareto manner. In addition, Monte-Carlo Tree Search (MCTS) is successful in computer Go and many other games. Hence, solving FJSP by MCTS is a new attempt. In this paper, we propose an MCTS algorithm for FJSP, by incorporating Variable Neighborhood Descent Algorithm and other techniques like Rapid Action Value Estimates Heuristic and Transposition Table. Our algorithm finds Pareto solutions of the benchmark problems proposed by Kacem et al. within 116 seconds: 4 solutions in 4x5, 3 in 10x7, 4 in 8x8, 4 in 10x10 and 2 in 15x10. These solutions are the same as the best found to date. Although one article claimed to have an extra 8x8 solution, that article did not find some of the above solutions.
Multistage Temporal Difference Learning for 2048-Like Games
(2017-12-01)
Yeh, Kun-Hao
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Wu, I-Chen
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Hsueh, Chu-Hsuan
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Chang, Chia-Chuan
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Liang, Chao-Chin
Szubert and Jaskowski successfully used temporal difference (TD) learning together with n-tuple networks for playing the game 2048. However, we observed a phenomenon that the programs based on TD learning still hardly reach large tiles. In this paper, we propose multistage TD (MS-TD) learning, a kind of hierarchical reinforcement learning method, to effectively improve the performance for the rates of reaching large tiles, which are good metrics to analyze the strength of 2048 programs. Our experiments showed significant improvements over the one without using MS-TD learning. Namely, using 3-ply expectimax search, the program with MS-TD learning reached 32768-tiles with a rate of 18.31%, while the one with TD learning did not reach any. After further tuned, our 2048 program reached 32768-tiles with a rate of 31.75% in 10,000 games, and one among these games even reached a 65536-tiles, which is the first ever reaching a 65536-tiles to our knowledge. In addition, MS-TD learning method can be easily applied to other 2048-like games, such as Threes. Based on MS-TD learning, our experiments for Threes also demonstrated similar performance improvement, where the program with MS-TD learning reached 6144-tiles with a rate of 7.83%, while the one with TD learning only reached 0.45%.
NEWS, INFORMATION, TOURNAMENTS, AND REPORTS 2048-BOT TOURNAMENT IN TAIWAN
(2014-09-01)
Yeh, Kun-Hao
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Liang, Chao-Chin
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Wu, Kuang-che
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Wu, I-Chen
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資訊工程學系
SOFTWARE DEVELOPMENT ARCHITECTURE FOR JOB-LEVEL ALGORITHMS
(2015-09-01)
Wei, Ting Han
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Liang, Chao-Chin
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Wu, I-Chen
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Chen, Lung-Pin
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資訊工程學系
Recently, Wu et al. introduced a general distributed computing approach, named Job-Level (JL) Computing. In JL computing, a search tree is maintained by a client process, while search tree nodes are evaluated, expanded, or generated by leveraging game-playing programs. These node operations are encapsulated as coarse-grain jobs, each requiring tens of seconds or more of running the programs. This article presents an abstraction of the JL computing approach and develops a general JL search framework so that common modules may be reused for various JL applications, making JL development easier. We describe in detail the implementation of the JL Proof-number Search (JL-PNS) and JL Upper Confidence Bound Search (JL-UCT) as case studies in the application of the JL search framework. In our case studies, only hundreds of lines of code are required for new JL applications, while the code for the JL framework consists of more than ten thousand lines of code. It demonstrates that this framework can be used to greatly reduce new JL application development and software maintenance efforts.