基于马尔可夫决策过程的人工智能与四人纸牌游戏 Big2 的出牌策略和自由出牌权探索

IF 2.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lien-Wu Chen;Yiou-Rwong Lu
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引用次数: 0

摘要

东亚流行的纸牌游戏Big2的规则不允许玩家查看对方的手牌,这使得人工智能在游戏中表现良好面临挑战。基于可以处理部分可观察和随机信息的马尔可夫决策过程(mdp),我们设计了Big2MDP框架来探索在最大化Big2游戏得分机会的同时最小化输牌风险的纸牌策略。根据我们对相关研究的回顾,这是第一个Big2人工智能框架,它具有以下特点:一是能够同时考虑得失分,以最小的损失风险做出最佳的制胜决策;二是能够预测多个对手的行动,优化决策;三是能够在关键时刻竞争改变牌组组合的自由发挥权的适应性。为了验证Big2MDP的可行性和有效性,我们在Android平台上实现了一个四人卡牌游戏Big2系统。实验结果表明,Big2MDP超越了现有的人工智能方法,在Big2游戏中与计算机和人类玩家竞争时取得了最高的胜率和最少的失分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Markov Decision Process-Based Artificial Intelligence With Card-Playing Strategy and Free-Playing Right Exploration for Four-Player Card Game Big2
The popular East Asian card game Big2 involves rules that do not allow players to view each other's hand cards, making artificial intelligence face challenges in performing well in this game. Based on Markov decision processes (MDPs) that can handle partially observable and stochastic information, we design the Big2MDP framework to explore card-playing strategies that minimize losing risks while maximizing scoring opportunities for the Big2 game. According to our review of relevant research, this is the first Big2 artificial intelligence framework with the following features: first, the ability to simultaneously consider scoring and losing points to make the best winning decisions with minimal losing risk, second, the capability to predict multiple opponents' actions to optimize the decision-making, and third, the adaptability to compete for the free-playing right to change card combinations at the essential moment. We implement a system of four-player card game Big2 on the Android platform to validate the feasibility and effectiveness of Big2MDP. Experimental results show that Big2MDP outperforms existing artificial intelligence methods, achieving the highest win rate and the least number of losing points as competing against both computer and human players in Big2 games.
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来源期刊
IEEE Transactions on Games
IEEE Transactions on Games Engineering-Electrical and Electronic Engineering
CiteScore
4.60
自引率
8.70%
发文量
87
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