基于知识的偏执搜索在欺骗行为中的应用
Knowledge-Based Paranoia Search in Trick-Taking
摘要：
本文提出了\emph{knowledge-based paraonoia search}（KBPS）在纸牌游戏Skat中的技巧采取过程中发现强迫赢，为一些最有趣的纸牌游戏之一的三个球员。它将有效的部分信息博弈树搜索与知识表示和推理相结合。这种最坏情况的分析，在少量的技巧后开始，导致优先选择卡。我们提供了KBPS的变种，为竞争者和对手，并近似找到一个强迫赢得对大多数世界的信念空间。通过回放数千场专家比赛，我们的评估表明，采用新算法的人工智能在比赛中的表现优于人类，在评估Skat锦标赛的商定标准（扩展的Seeger系统）中，平均得分超过1,000分。
英文摘要：
This paper proposes \emph{knowledge-based paraonoia search} (KBPS) to find forced wins during trick-taking in the card game Skat; for some one of the most interesting card games for three players. It combines efficient partial information game-tree search with knowledge representation and reasoning. This worst-case analysis, initiated after a small number of tricks, leads to a prioritized choice of cards. We provide variants of KBPS for the declarer and the opponents, and an approximation to find a forced win against most worlds in the belief space. Replaying thousands of expert games, our evaluation indicates that the AIs with the new algorithms perform better than humans in their play, achieving an average score of over 1,000 points in the agreed standard for evaluating Skat tournaments, the extended Seeger system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
