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作 者:刘航 丁濛[1,2] 李淑琴 LIU Hang;DING Meng;LI Shuqin(Computer School,Beijing Information Science&Technology University,Beijing 100101,China;Perception and Computational Intelligence Laboratory,Beijing 100101,China)
机构地区:[1]北京信息科技大学计算机学院,北京100101 [2]感知与计算智能联合实验室,北京100101
出 处:《重庆理工大学学报(自然科学)》2024年第5期162-169,共8页Journal of Chongqing University of Technology:Natural Science
摘 要:针对现有二打一叫牌决策研究中分类粒度低的问题,提出了一种基于多模型堆叠与关键特征提取的叫牌模型训练方案。具体来说,设计了手牌特征构建方法,即由手牌向量、牌型特征、手牌工整度、最小出牌步数以及组合丰富度共同构成玩家手牌特征;在此基础上,提出了使用堆叠法融合4类基模型的决策结果,并训练二层模型CatBoost给出最终决策。实验结果表明:相较于仅使用手牌向量特征,该特征构建方式可显著提升模型性能。经过堆叠法融合多模型决策后,二层模型准确率进一步提升,最终在测试集上达到84.3854%的精准度。所提方法可为其他牌类游戏的叫牌博弈决策提供参考。Addressing the granularity limitation observed in existing research on“Fighting the Landlord”bidding decision problem,this paper proposes an approach for training a Bid Recommendation Model.Specifically,a methodology is devised for constructing hand features,including hand vector,hand pattern features,hand tidiness,minimum step in card play,and combination richness.Based on this,we propose a stacked approach to fuse the decision results of four base models and train a meta classifier CatBoost as the final decision model for the bidding decision.Our experimental results indicate that,in comparison with relying solely on hand vector features,this feature construction method significantly enhances model performance.Following the fusion of multiple model decisions through stacking,the accuracy of the second-layer model is further improved,achieving a precision of 84.3854%on the test set.Moreover,this method provides some references for bidding decision in other card games.
分 类 号:TP311[自动化与计算机技术—计算机软件与理论]
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