Bidding in Spades
This work addresses a specific problem in game AI for Spades players and developers, but it is incremental as it focuses on bidding within an existing game framework.
The paper tackles the challenge of bidding in the card game Spades by developing the BIS algorithm, which uses domain-specific heuristics corrected with machine learning to maximize winning probability, resulting in superiority over recreational human players and rule-based bots.
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.