# Opponent Modeling in Multiplayer Imperfect-Information Games

arXiv: https://arxiv.org/abs/2212.06027 · HTML: https://mlsanity.com/paper/2212.06027/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Bayesian Opponent Exploitation in Imperfect-Information Games](https://mlsanity.com/paper/1603.03491/) (2016)
- [Solving Large Extensive-Form Games with Strategy Constraints](https://mlsanity.com/paper/1809.07893/) (2018)

## Built on by (most cited first, 1 of 1)

- [Consistent Opponent Modeling in Imperfect-Information Games](https://mlsanity.com/paper/2508.17671/) (2025)

## Abstract (arXiv)

In many real-world settings agents engage in strategic interactions with multiple opposing agents who can employ a wide variety of strategies. The standard approach for designing agents for such settings is to compute or approximate a relevant game-theoretic solution concept such as Nash equilibrium and then follow the prescribed strategy. However, such a strategy ignores any observations of opponents' play, which may indicate shortcomings that can be exploited. We present an approach for opponent modeling in multiplayer imperfect-information games where we collect observations of opponents' play through repeated interactions. We run experiments against a wide variety of real opponents and exact Nash equilibrium strategies in three-player Kuhn poker and show that our algorithm significantly outperforms all of the agents, including the exact Nash equilibrium strategies.