Bidding in Spades

TL;DR

This paper introduces BIS, a bidding algorithm for Spades that outperforms recreational players and existing bots by combining heuristics with machine learning to estimate expected utilities for bids.

Contribution

The paper presents a novel bidding algorithm for Spades that integrates heuristics and machine learning, improving over rule-based bots and recreational players.

Findings

BIS outperforms rule-based bidding bots.

BIS surpasses recreational human players.

The approach effectively combines heuristics with machine learning.

Abstract

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.