In multi-player card games such as Skat or Bridge, the early stages of the game, such as bidding, game selection, and initial card selection, are often more critical to the success of the play than refined middle- and end-game play. At the current limits of computation, such early decision-making resorts to using statistical information derived from a large corpus of human expert games. In this paper, we derive and evaluate a general bootstrapping outer-learning framework that improves prediction accuracy by expanding the database of human games with millions of self-playing AI games to generate and merge statistics. We implement perfect feature hash functions to address compacted tables, producing a self-improving card game engine, where newly inferred knowledge is continuously improved during self-learning. The case study in Skat shows that the automated approach can be used to support various decisions in the game.

翻译：在斯卡特牌或桥牌等多玩家纸牌游戏中，游戏的早期阶段（如叫牌、游戏选择与初始牌张选择）通常比精细的中局与终局打法对胜负更具决定性。在当前计算能力限制下，此类早期决策依赖于从大量人类专家对局语料库中提取的统计信息。本文提出并评估了一种通用的自举外部学习框架，该框架通过将人类对局数据库扩展至数百万局自我对弈的AI对局以生成并融合统计信息，从而提升预测准确性。我们实现了完美的特征哈希函数以处理压缩表，构建了一个自我改进的纸牌游戏引擎，其中新推断的知识在自我学习过程中持续优化。斯卡特牌的案例研究表明，该自动化方法可用于支持游戏中的多种决策。