Jr-Chang Chen

Equivalence Classes in Chinese Dark Chess Endgames

Chinese Dark Chess, a nondeterministic two-player game, has not been studied thoroughly. State-of-the-art programs focus on using search algorithms to explore the probability behavior of flipping unrevealed pieces in the opening and the midgame phases. There has been comparatively little research on opening books and endgame databases, especially endgames with nondeterministic flips. In this paper, we propose an equivalence relation that classifies the complex piece relations between the material combinations of each player, and derive a partition for all such material combinations. The technique can be applied to endgame database compression to reduce the number of endgames that need to be constructed. As a result, the computation time and the size of endgame databases can be reduced substantially. Furthermore, understanding the piece relations facilitates the development of a well-designed evaluation function and enhances the search efficiency. In Chinese Dark Chess, the number of nontrivial material combinations comprised of only revealed pieces is 8 497 176, and the number that contain at least one unrevealed piece is 239 980 775 397. Under the proposed method, the compression rates of the above material combinations reach 28.93% and 42.52%, respectively; if the method is applied to endgames comprised of three to eight pieces, the compression rates reach 5.82% and 5.98%, respectively.

Design and Implementation of Chinese Dark Chess Programs

Chinese Dark Chess is an old and very popular game in the Chinese culture sphere. This game is a stochastic game with symmetric hidden information. This paper reviews alpha-beta search with chance nodes and proposes heuristics on Chinese Dark Chess programs. We propose an application of nondeterministic Monte Carlo Tree Search with random nodes for tackling partial observation. The proposed methods were implemented in the program Diablo, which won four Chinese Dark Chess tournaments in TAAI 2011/2012, TCGA 2011/2012 computer game tournaments. Diablo also played hundreds of games with different human players and programs based on alpha-beta search. These results show that the nondeterministic MCTS equipped with our heuristics is promising for Chinese Dark Chess.

Job-Level Alpha-Beta Search

An approach called generic job-level (JL) search was proposed to solve computer game applications by dispatching jobs to remote workers for parallel processing. This paper applies JL search to alpha-beta search, and proposes a JL alpha-beta search (JL-ABS) algorithm based on a best-first search version of MTD(f). The JL-ABS algorithm is demonstrated by using it in an opening book analysis for Chinese chess. The experimental results demonstrated that JL-ABS reached a speed-up of 10.69 when using 16 workers in the JL system.

The Art of the Chinese Dark Chess Program DIABLE

Diable is a famous Chinese dark chess program, which won the Chinese dark chess tournaments in TAAI 2011, TCGA 2011, and TCGA2012 computer game tournaments. Chinese dark chess is an old and very popular game in Chinese culture sphere. This game is played with imperfect information. Most computer Chinese dark chess programs used alpha-beta search with chance nodes to deal with the imperfect information. Diable used a new nondeterministic Monte Carlo tree search model for Chinese dark chess. These tournament results show that the nondeterministic Monte Carlo tree search is promising for Chinese dark chess.

Optimization of Nonogram's Solver by Using an Efficient Algorithm

Nonogram is an extremely popular game of logic in Japan and Holland. Solving a Nonogram is difficult because it is an NP-complete problem. Some studies have applied logical rules, and then, to improve the searching efficiency, used Backtracking to solve the cells that logical rules provides a method that is based on the basic rules to solve for Nonogram puzzle quickly. Our method is an easy and fast algorithm. A database and our algorithm, can be used together to solve Nonograms at very high speed. Experimental results show that our algorithm can successfully and efficiently solve Nonograms.

Strength Improvement and Analysis for an MCTS-Based Chinese Dark Chess Program

Monte-Carlo tree search (MCTS) has been successfully applied to Chinese dark chess (CDC). In this paper, we study how to improve and analyze the playing strength of an MCTS-based CDC program, named DarkKnight, which won the CDC tournament in the 17th Computer Olympiad. We incorporate the three recent techniques, early playout terminations, implicit minimax backups, and quality-based rewards, into the program. For early playout terminations, playouts end when reaching states with likely outcomes. Implicit minimax backups use heuristic evaluations to help guide selections of MCTS. Quality-based rewards adjust rewards based on online collected information. Our experiments showed that the win rates against the original DarkKnight were 60.75 %, 70.90 % and 59.00 %, respectively for incorporating the three techniques. By incorporating all together, we obtained a win rate of 76.70 %.

Advanced Meta-knowledge for Chinese Chess Endgame Knowledge Bases

The proper evaluation of a possible transition from the middle game to the endgame is an important research issue. We constructed an endgame knowledge base that consists of a large set of endgame heuristics that supports the evaluation. Moreover, a general graph model is proposed to resolve conflicts between two competing material combinations. However, it turned out to be difficult to find such competing material combinations. We need better meta-knowledge rules to find more potential conflicts. In this article, we propose five meta-knowledge rules for Chinese chess. Two examples of meta-knowledge rules are piece exchanges and pawn exchanges. The meta-knowledge rules are inducted from real games played by masters. The heuristics so found are endowed with confidence factors to show their chances of being correct. Using this method, 20% of a previous constructed body of endgame knowledge, consisting of 124, 747 material combinations, was found to be erroneous. About 82.13% of these heuristic errors are auto-corrected using our algorithm. By using the corrected knowledge base, the strength of our game-playing program, CONTEMPLATION, is clearly improved according to self-play tests.

Multilevel Inference in Chinese Chess Endgame Knowledge Bases

In Chinese chess, retrograde analysis can be used to solve complex elementary (i.e., fundamental) endgames and to provide perfect play. However, there are still many practical endgames pending to be analysed due to problems related to the complex playing rules. Of course, there is heuristic endgame knowledge for the evaluation functions. This knowledge is often applied to the complex endgames or the real endgames to improve the playing strength. One crucial problem is to choose relatively advantageous endgames by selecting appropriate piece exchanges. For this problem, we designed a Chinese chess endgame knowledge-based system with a large set of endgame heuristics, called an endgame knowledge base. We use this knowledge base in our program, CONTEMPLATION. To maintain the quality of the constructed knowledge base, it is important to detect and resolve conflicts between the heuristics. A conflict-resolution method enables Chinese chess experts to correct erroneous entries by using knowledge about two endgames that differ by precisely one piece. The problem involves detecting potential errors so that a human expert can easily revise and improve the reliability of the knowledge base. In this article, we introduce two major enhancements to the above method. First, we propose a general graph model to handle the heuristics when the endgames involved are differing in more than one piece. Second, we add a confidence-factor parameter to encode the probability that a heuristic may be true. Such heuristics are often used in real games when pieces are exchanged. The resulting graph model is effective in maintaining the consistency of predefined meta-knowledge, and thus improves the overall quality significantly. The results of the experiments on self-play tests demonstrate that the derived knowledge base improves the playing strength of CONTEMPLATION.

An Intelligent Tutoring System of Chinese Chess

Computer Chinese chess is an application of artificial intelligence. The playing strength of many Chinese chess programs is at the level of human masters or grandmasters. However, it is not easy for a human player to learn Chinese chess skills from these strong programs because their outputs are no more than moves and score values. It is necessary for a student to understand why he or she loses the game and to receive feedback after practice. In this paper, we propose an intelligent tutoring system for learning Chinese chess. The system interacts with students by playing games with them and gives comments and suggestions to them during a game without any human intervention. After some iterations of practice, our system reports their learning achievements by analyzing their game records.

An analysis for strength improvement of an MCTS-based program playing Chinese dark chess

Monte Carlo tree search (MCTS) has been successfully applied to many games recently. Since then, many techniques are used to improve the strength of MCTS-based programs. This paper investigates four recent techniques: early playout terminations, implicit minimax backups, quality-based rewards and progressive bias. The strength improvements are analyzed by incorporating the techniques into an MCTS-based program, named DarkKnight, for Chinese Dark Chess. Experimental results showed that the win rates against the original DarkKnight were 60.75%, 71.85%, 59.00%, and 82.10%, respectively for incorporating the four techniques. The results indicated that the improvement by progressive bias was most significant. By incorporating all together, a better win rate of 84.75% was obtained.