An efficient AI-based method to play the Mahjong game with the knowledge and game-tree searching strategy

The Mahjong game has widely been acknowledged to be a difficult problem in the field of imperfect information games. Because of its unique characteristics of asymmetric, serialized and multi-player game information, conventional methods of dealing with perfect information games are difficult to be applied directly on the Mahjong game. Therefore, AI (artificial intelligence)-based studies to handle the Mahjong game become challenging. In this study, an efficient AI-based method to play the Mahjong game is proposed based on the knowledge and game-tree searching strategy. Technically, we simplify the Mahjong game framework from multi-player to single-player. Based on the above intuition, an improved search algorithm is proposed to explore the path of winning. Meanwhile, three node extension strategies are proposed based on heuristic information to improve the search efficiency. Then, an evaluation function is designed to calculate the optimal solution by combining the winning rate, score and risk value assessment. In addition, we combine knowledge and Monte Carlo simulation to construct an opponent model to predict hidden information and translate it into available relative probabilities. Finally, dozens of experiments are designed to prove the effectiveness of each algorithm module. It is also worthy to mention that, the first version of the proposed method, which is named as KF-TREE, has won the silver medal in the Mahjong tournament of 2019 Computer Olympiad.

A game strategy model in the digital curling system based on NFSP

The digital curling game is a two-player zero-sum extensive game in a continuous action space. There are some challenging problems that are still not solved well, such as the uncertainty of strategy, the large game tree searching, and the use of large amounts of supervised data, etc. In this work, we combine NFSP and KR-UCT for digital curling games, where NFSP uses two adversary learning networks and can automatically produce supervised data, and KR-UCT can be used for large game tree searching in continuous action space. We propose two reward mechanisms to make reinforcement learning converge quickly. Experimental results validate the proposed method, and show the strategy model can reach the Nash equilibrium.

On the use of troglomorphic characters in Namanereidinae (Annelida; Nereididae) systematics

The Namanereidinae are one of the most successful polychaete groups to colonize subterranean waters. Many species have evolved adaptations to underground life including elongation of appendages and reduction of eyes and pigmentation. However, the use of these troglomorphic characters in the group’s systematics is contentious. The present study conducts a series of tree searching and phylogeny reconstructions to evaluate the influence of these characters in the phylogeny of the group. Results show that troglomorphic characters cause no serious errors in the phylogenetic reconstruction of Namanereidinae, and support the two existing genera, which can be unequivocally distinguished by non-troglomorphic traits. As a consequence of this phylogenetic hypothesis the following taxonomic changes are required: Lycastoides becomes a junior synonym of Namanereis, and its only species becomes a new combination, N. alticola n. comb. and Namalycastis occulta is moved to Namanereis becoming Namanereis occulta n. comb.