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Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games. Among these games, DouDizhu, a popular card game in China, is very challenging due to the imperfect…

人工智能 · 计算机科学 2022-04-07 Youpeng Zhao , Jian Zhao , Xunhan Hu , Wengang Zhou , Houqiang Li

Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents. While significant achievements have been made in various perfect- and imperfect-information games, DouDizhu (a.k.a. Fighting…

人工智能 · 计算机科学 2021-06-14 Daochen Zha , Jingru Xie , Wenye Ma , Sheng Zhang , Xiangru Lian , Xia Hu , Ji Liu

Artificial intelligence for card games has long been a popular topic in AI research. In recent years, complex card games like Mahjong and Texas Hold'em have been solved, with corresponding AI programs reaching the level of human experts.…

人工智能 · 计算机科学 2024-09-16 Chang Lei , Huan Lei

Card game AI has always been a hot topic in the research of artificial intelligence. In recent years, complex card games such as Mahjong, DouDizhu and Texas Hold'em have been solved and the corresponding AI programs have reached the level…

人工智能 · 计算机科学 2022-11-01 Yudong Lu , Jian Zhao , Youpeng Zhao , Wengang Zhou , Houqiang Li

Deep reinforcement learning has made significant progress in games with imperfect information, but its performance in the card game Doudizhu (Chinese Poker/Fight the Landlord) remains unsatisfactory. Doudizhu is different from conventional…

人工智能 · 计算机科学 2024-03-22 Yiquan Chen , Yingchao Lyu , Di Zhang

The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period. Recent advancements have propelled AI programs to showcase expertise in intricate card games such as…

人工智能 · 计算机科学 2023-12-06 Youpeng Zhao , Yudong Lu , Jian Zhao , Wengang Zhou , Houqiang Li

As a challenging multi-player card game, DouDizhu has recently drawn much attention for analyzing competition and collaboration in imperfect-information games. In this paper, we propose PerfectDou, a state-of-the-art DouDizhu AI system that…

人工智能 · 计算机科学 2024-02-29 Guan Yang , Minghuan Liu , Weijun Hong , Weinan Zhang , Fei Fang , Guangjun Zeng , Yue Lin

Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI. In recent years, studies on game AI have gradually evolved from relatively simple environments…

人工智能 · 计算机科学 2020-04-02 Junjie Li , Sotetsu Koyamada , Qiwei Ye , Guoqing Liu , Chao Wang , Ruihan Yang , Li Zhao , Tao Qin , Tie-Yan Liu , Hsiao-Wuen Hon

Games are a simplified model of reality and often serve as a favored platform for Artificial Intelligence (AI) research. Much of the research is concerned with game-playing agents and their decision making processes. The game of Guandan…

人工智能 · 计算机科学 2024-02-22 Yifan Yanggong , Hao Pan , Lei Wang

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains. This presents us with an opportunity to further human knowledge and improve human expert performance by leveraging…

人工智能 · 计算机科学 2023-10-26 Lisa Schut , Nenad Tomasev , Tom McGrath , Demis Hassabis , Ulrich Paquet , Been Kim

Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker is the quintessential game of imperfect…

The game of Go has long served as a benchmark for artificial intelligence, demanding sophisticated strategic reasoning and long-term planning. Previous approaches such as AlphaGo and its successors, have predominantly relied on model-based…

人工智能 · 计算机科学 2026-01-08 Jingbin Liu , Xuechun Wang

The evaluation function for imperfect information games is always hard to define but owns a significant impact on the playing strength of a program. Deep learning has made great achievements these years, and already exceeded the top human…

人工智能 · 计算机科学 2019-06-10 Shiqi Gao , Fuminori Okuya , Yoshihiro Kawahara , Yoshimasa Tsuruoka

We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not…

The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation…

An imperfect-information game is a type of game with asymmetric information. It is more common in life than perfect-information game. Artificial intelligence (AI) in imperfect-information games, such like poker, has made considerable…

人工智能 · 计算机科学 2024-05-29 Qibin Zhou , Dongdong Bai , Junge Zhang , Fuqing Duan , Kaiqi Huang

Many poker systems, whether created with heuristics or machine learning, rely on the probability of winning as a key input. However calculating the precise probability using combinatorics is an intractable problem, so instead we approximate…

人工智能 · 计算机科学 2018-08-24 Brandon Da Silva

In the past few years, AlphaZero's exceptional capability in mastering intricate board games has garnered considerable interest. Initially designed for the game of Go, this revolutionary algorithm merges deep learning techniques with the…

人工智能 · 计算机科学 2023-09-06 Wen Liang , Chao Yu , Brian Whiteaker , Inyoung Huh , Hua Shao , Youzhi Liang

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a…

Poker is in the family of imperfect information games unlike other games such as chess, connect four, etc which are perfect information game instead. While many perfect information games have been solved, no non-trivial imperfect…

计算机科学与博弈论 · 计算机科学 2024-01-15 Prathamesh Sonawane , Arav Chheda
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