Challenging Human Supremacy in Skat

Abstract

After impressive successes in deterministic and fully-observable board games to significantly outclass humans, game playing research shifts towards non-deterministic and imperfect information card games, where humans are still persistently better. In this paper we devise a player that challenges human supremacy in Skat. We provide a complete player for playing selected variants of the game, with effective solutions for bidding and Skat putting, extracting knowledge from several million games. For trick play we combine expert rules with engineered tree exploration for optimal open card play. For dealing with uncertainty especially in Ouvert games we search the belief space.