Knowledge-Based Paranoia Search in Trick-Taking
  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.
