Knowledge-Based Paranoia Search in Trick-Taking
This is an incremental improvement for Skat players and AI researchers, as it enhances AI performance in a specific card game domain.
The paper tackled the problem of finding forced wins in the trick-taking card game Skat by proposing knowledge-based paranoia search (KBPS), which combines game-tree search with knowledge representation. The result was that AIs using these algorithms outperformed human experts, achieving an average score of over 1,000 points in the standard Skat tournament evaluation system.
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.