Shi-Jim Yen

Current Frontiers in Computer Go

This paper presents the recent technical advances in Monte Carlo tree search (MCTS) for the game of Go, shows the many similarities and the rare differences between the current best programs, and reports the results of the Computer Go event organized at the 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2009), in which four main Go programs played against top level humans. We see 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 (but not solved) weaknesses of the current algorithms. Applications far from the game of Go are also cited. Importantly, the first ever win of a computer against a 9th Dan professional player in 9 × 9 Go occurred in this event.

Two-Stage Monte Carlo Tree Search for Connect6

Recently, Monte Carlo tree search (MCTS) has become a well-known game search method, and has been successfully applied to many games. This method performs well in solving search trees with numerous branches, such as Go, Havannah, etc. Connect6 is a game involving a search tree with numerous branches, and it is also one of the sudden-death games. This paper thus proposes a new MCTS variant related to Connect6, called two-stage MCTS. The first stage focuses on threat space search (TSS), which is designed to solve the sudden-death problem. For the double-threat TSS in Connect6, this study proposes an algorithm called iterative threat space search (ITSS) which combines normal TSS with conservative threat space search (CTSS). The second stage uses MCTS to estimate the game-theoretic value of the initial position. This stage aims at finding the most promising move. The experimental result shows that two-stage MCTS is considerably more efficient than traditional MCTS on those positions with TSS solution in Connect6. Furthermore, according to Connect6 heuristic knowledge, this paper uses relevance-zone search to accelerate identifying winning and losing moves.

T2FS-Based Adaptive Linguistic Assessment System for Semantic Analysis and Human Performance Evaluation on Game of Go

The game of Go is a board game with a long history that is much more complex than chess. The uncertainties of this game will be higher when the board size gets bigger. For evaluating the human performance on Go games, one human could be advanced to a higher rank based on the number of winning games via a formal human against human competition. However, a human Go players performance could be influenced by factors such as the on-the-spot environment, as well as physical and mental situations of the day, which causes difficulty and uncertainty in certificating the humans rank. Thanks to a sample of one players games, evaluating his/her strength by classical models such as the Bradley-Terry model is possible. However, due to inhomogeneous game conditions and limited access to archives of games, such estimates can be imprecise. In addition, classical rankings (1 Dan, 2 Dan, ...) are integers, which lead to a rather imprecise estimate of the opponents strengths. Therefore, we propose to use a sample of games played against a computer to estimate the humans strength. In order to increase the precision, the strength of the computer is adapted from one move to the next by increasing or decreasing the computational power based on the current situation and the result of games. The human can decide some specific conditions, such as komi and board size. In this paper, we use type-2 fuzzy sets (T2FSs) with parameters optimized by a genetic algorithm for estimating the rank in a stable manner, independently of board size. More precisely, an adaptive Monte Carlo tree search (MCTS) estimates the number of simulations, corresponding to the strength of its opponents. Next, the T2FS-based adaptive linguistic assessment system infers the human performance and presents the results using the linguistic description. The experimental results show that the proposed approach is feasible for application to the adaptive linguistic assessment on a human Go players performance.

GOLOIS wins Phantom-Go Tournament

Phantom Go is a two-player game often played as an entertaining variation by regular Go players. It is the equivalent of Kriegspiel for Chess. Phantom Go is a variant of classic Go in which the players do not see the opponent’s moves. Two players each have their own board, on which only their own stones are visible. A referee is needed. He has a reference board on which the actual state of the game is maintained. The players communicate their move decisions to the referee. There are a few variations to the rules of Phantom Go, in the rules used for the tournament, the referee has the following replies to a suggested move:

A type-2 fuzzy personal ontology for meeting scheduling system

Meeting schedule contains a variety of features, such as personal constraints, preferences, and even the personal calendar. In an organization, the host always needs to spend much time negotiating with the potential attendees to acquire the most suitable time slot to hold the meeting. The process is very time-consuming and effort-requiring so that this paper proposes a type-2 fuzzy personal ontology, a type-2 meeting scheduling ontology, and a decision supported multi-agent to facilitate an organizations meeting scheduling process. Additionally, the fuzzy markup language (FML) is used to describe the knowledge base and rule base of the proposed meeting schedule system, and then each potential attendees meeting-attending possibility is inferred through the fuzzy inference mechanism. Finally, a semantic description is generated to let the host roughly know each persons meeting attendance possibility. The experimental results show that the proposed approach is feasible for meeting scheduling.

Human vs. Computer Go: Review and Prospect [Discussion Forum]

The Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development. This article discusses the development of computational intelligence (CI) and its relative strength in comparison with human intelligence for the game of Go. We first summarize the milestones achieved for computer Go from 1998 to 2016. Then, the computer Go programs that have participated in previous IEEE CIS competitions as well as methods and techniques used in AlphaGo are briefly introduced. Commentaries from three high-level professional Go players on the five AlphaGo versus Lee Sedol games are also included. We conclude that AlphaGo beating Lee Sedol is a huge achievement in artificial intelligence (AI) based largely on CI methods. In the future, powerful computer Go programs such as AlphaGo are expected to be instrumental in promoting Go education and AI real-world applications.

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.

An Ontology-based Fuzzy Inference System for Computer Go Applications

In order to stimulate the development and research in computer Go, several Taiwanese Go players were invited to play against some famous computer Go programs. Those competitions revealed that the ontology model for Go game might resolve problems happened in the competitions. Therefore, this paper presents a Go game record ontology and Go board ontology schemes. An ontology-based fuzzy inference system is also developed to provide the regional alarm level for a Go beginner or a computer Go program in order to place the stone at the much more appropriate position. Experimental results indicate that the proposed approach is feasible for computer Go application. Hopefully, advances in the intelligent agent and the ontology model can provide a significant amount of knowledge to make a progress in computer Go program and achieve as much as computer chess or Chinese chess in the future.

TAAI 2011 COMPUTER-GAME TOURNAMENTS

th , 2013. In this event, fifty computer programs from the Netherlands, France, and Taiwan participated in tournaments for ten different games being Go 9x9, Chinese chess, Connect6, Chinese dark chess, Mahjong, NoGo, MiniShogi, Nonogram, Othello, and Double King Dark Chess. The participants and the final results are listed in Table 1. With much pleasure the organizer invited Prof. Yoshimasa Tsuruka of the University of Tokyo for the keynote speech. Yoshimasa is a developer of the well-known Shogi program, GEKISASHI (激指), which is the four-times World Computer Shogi Champion (WCSC). The title of his talk was Recent Advances in Algorithms for Traditional Board and Table Games. In Table 1, the team from the National Chiao Tung University (NCTU) performed best by winning four gold medals. Each of the other teams was awarded at least one gold medal. This showed that each team has focused on specific games during 2013 and did so obtain good achievements. The champion and the runner-up of Chinese chess tournament were SHIGA and CHIMO, respectively. They had also the same ranks in the 17 th Computer Olympiad and in TAAI 2012. SHIGA won all games except that only one game against CHIMO ended up in a draw in this tournament. SHIGA was designed to support the multiprocessor architecture, and its search depth reached 20 plies while using a 4.5GHz 12-core computer. CHIMO used an opening book compiled from 85,000 game records that was then enhanced by the JL-ABS algorithm. Its evaluation function was improved and was tuned better. Both programs were estimated to be near 8-dan. SHARK as a newcomer won the bronze medal. Chinese dark chess is popular in China and Taiwan. It uses the same board and pieces as Chinese chess, but the pieces can be either revealed or unrevealed (Chen et al., 2010). In 2013, nine programs from France and Taiwan participated. The competitions were played automatically on the game-playing platform designed by Jr-Chang Chen. The platform was also used in the 17 th Computer Olympiad. YAHARI, which landed at the third place in the 17 th Computer Olympiad, became the champion; and SGTDARK was the runner-up. Both programs are based on alpha-beta search (ABS) and used endgame databases constructed by retrograde analysis. The third place was for DARKKNIGHT, which also won the bronze medal in TAAI 2012. DARKKNIGHT was based on Monte-Carlo tree search (MCTS) and utilized the concept of chance nodes to deal with flipping unrevealed pieces. The playing strength of each program improved significantly, and the programs based on ABS performed slightly better than those based on MCTS.

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.