# Policy Based Inference in Trick-Taking Card Games

## TL;DR

This paper introduces a Policy Based Inference algorithm that uses player modeling to improve state inference in trick-taking card games, significantly enhancing AI performance in Skat.

## Contribution

The paper presents a novel Policy Based Inference method that leverages player modeling to improve state estimation in large imperfect-information games.

## Key findings

- PI algorithm vastly improves inference accuracy
- Enhanced AI performance in Skat using PI in determinized search
- Demonstrated superiority over previous inference methods

## Abstract

Trick-taking card games feature a large amount of private information that slowly gets revealed through a long sequence of actions. This makes the number of histories exponentially large in the action sequence length, as well as creating extremely large information sets. As a result, these games become too large to solve. To deal with these issues many algorithms employ inference, the estimation of the probability of states within an information set. In this paper, we demonstrate a Policy Based Inference (PI) algorithm that uses player modelling to infer the probability we are in a given state. We perform experiments in the German trick-taking card game Skat, in which we show that this method vastly improves the inference as compared to previous work, and increases the performance of the state-of-the-art Skat AI system Kermit when it is employed into its determinized search algorithm.