Cheng-Wei Chou

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:

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.

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.

Strategic Choices: Small Budgets and Simple Regret

In many decision problems, there are two levels of choice: The first one is strategic and the second is tactical. We formalize the difference between both and discuss the relevance of the bandit literature for strategic decisions and test the quality of different bandit algorithms in real world examples such as board games and card games. For exploration-exploitation algorithm, we evaluate the Upper Confidence Bounds and Exponential Weights, as well as algorithms designed for simple regret, such as Successive Reject. For the exploitation, we also evaluate Bernstein Races and Uniform Sampling. As for the recommandation part, we test Empirically Best Arm, Most Played, Lower ConfidenceBounds and Empirical Distribution. In the one-player case, we recommend Upper Confidence Bound as an exploration algorithm (and in particular its variants adaptUCBE for parameter-free simple regret) and Lower Confidence Bound or Most Played Arm as recommendation algorithms. In the two-player case, we point out the commodity and efficiency of the EXP3 algorithm, and the very clear improvement provided by the truncation algorithm TEXP3. Incidentally our algorithm won some games against professional players in kill-all Go (to the best of our knowledge, for the first time in computer games).

A Simple and Rapid Lights-up Solver

This study proposes a solver that mix pattern matching method with elimination search to solve Lights-up puzzles. First, the pattern matching method is used to solve some parts of puzzle in a very short time. Then, the elimination search method is applied to solve the part in which the former method cannot be applied. The solver is authenticated by solving problems taken from Internet and compared with methods in other studies. The proposed approach is fast and efficient.

Towards a Solution of 7x7 Go with Meta-MCTS

Solving board games is a hard task, in particular for games in which classical tools such as alpha-beta and proof-number-search are somehow weak. In particular, Go is not solved (in any sense of solving, even the weakest) beyond 6x6. We here investigate the use of Meta-Monte-Carlo-Tree-Search, for building a huge 7x7 opening book. In particular, we report the twenty wins (out of twenty games) that were obtained recently in 7x7 Go against pros; we also show that in one of the games, with no human error, the pro might have won.

THE IEEE SSCI 2011 HUMAN VS. COMPUTER-GO COMPETITION

th to 14 th 2011 at Paris, France. There were several games in this competition, including 13x13 Go, 19x19 Go, Blind Go, and Random Go games. This report focuses on 13x13 Go and 19x19 Go. For Blind Go and Random Go, please refer to the report by Yen et al. (2011). The human participants Chun-Hsun Chou and PingChiang Chou are brothers. They both are professional Go Players. Table 1 shows information on the human participants. Table 2 shows information of the two computer Go programs, MOGO and PACHI.

Elimination search for puzzle games: An application for Hashi solver

This paper proposes an efficient method to solve Hashi, a logical-type puzzle game with N by M grid. By using two methods, intersection method and elimination search, we can solve Hashi quickly and efficiency. The solver is authenticated by solving problems taken from Internet.