# Bidding in Spades

arXiv: https://arxiv.org/abs/1912.11323 · HTML: https://mlsanity.com/paper/1912.11323/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs](https://mlsanity.com/paper/2302.12676/) (2023)
- [On the Power of Refined Skat Selection](https://mlsanity.com/paper/2104.02997/) (2021)
- [Knowledge-Based Paranoia Search in Trick-Taking](https://mlsanity.com/paper/2104.05423/) (2021)
- [BridgeHand2Vec Bridge Hand Representation](https://mlsanity.com/paper/2310.06624/) (2023)
- [Outer-Learning Framework for Playing Multi-Player Trick-Taking Card Games: A Case Study in Skat](https://mlsanity.com/paper/2512.15435/) (2025)

## Abstract (arXiv)

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, textitbidding and textitplaying. 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 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.