Bidding in Spades
  We present a Spades bidding algorithm that is superior to recreational human
players and to publicly available bots. Like in Bridge, the game of Spades is
composed of two independent phases, \textit{bidding} and \textit{playing}. This
paper focuses on the bidding algorithm, since this phase holds a precise
challenge: based on the input, choose the bid that maximizes the agent's
winning probability. Our \emph{Bidding-in-Spades} (BIS) algorithm heuristically
determines the bidding strategy by comparing the expected utility of each
possible bid. A major challenge is how to estimate these expected utilities. To
this end, we propose a set of domain-specific heuristics, and then correct them
via machine learning using data from real-world players. The \BIS algorithm we
present can be attached to any playing algorithm. It beats rule-based bidding
bots when all use the same playing component. When combined with a rule-based
playing algorithm, it is superior to the average recreational human.
