用AI自对弈扩充人类牌局数据，提升叫牌决策准确率
Outer-Learning Framework for Playing Multi-Player Trick-Taking Card Games: A Case Study in Skat
- 通过自对弈生成百万级新牌局，扩展人类专家数据集
- 在斯卡特游戏中，早期决策准确率显著提升
- 适合研究卡牌博弈与强化学习的开发者参考
在多玩家纸牌游戏如斯卡特或桥牌中，叫牌、选局和初期出牌等早期阶段往往比中后期策略更决定胜负。受限于当前计算能力，这些早期决策通常依赖大量人类专家对局的统计信息。本文提出并评估了一种通用的外层学习框架，通过生成数百万场AI自对弈游戏来扩充人类对局数据库，融合生成的统计数据以提升预测准确性。我们设计了精确的特征哈希函数，解决状态表压缩问题，构建了一个可自我迭代优化的纸牌引擎，使新推断的知识在自学习过程中持续改进。斯卡特游戏的案例研究表明，该自动化方法能有效支持多种游戏决策。
原文摘要 · Abstract (English)
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.
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