Valet: A Standardized Testbed of Traditional Imperfect-Information Card Games

2026-03-03

AI Summary

This work addresses the lack of a unified evaluation benchmark for imperfect-information game AI across diverse games. To this end, the authors introduce Valet, a standardized testbed encompassing 21 traditional card games that span varied rulesets, cultural origins, and game mechanisms. For the first time, these games are uniformly modeled using the RECYCLE description language. Leveraging this framework, the study quantifies key properties—such as branching factor and game length—via random simulation and Monte Carlo Tree Search (MCTS), and establishes baseline performance distributions of MCTS against random opponents. Valet provides the first systematic, reproducible benchmark for evaluating the generality and robustness of algorithms in multi-cultural, multi-mechanism imperfect-information settings.

Technology Category

Game Theory and Economic Paradigms: Imperfect InformationMultiagent Systems: Mechanism DesignHumans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsEconomics, Online Markets and Human Computation: Research challenges in human and human-AI computationWeb Mining and Content Analysis: Web data generation and simulation

Abstract

AI algorithms for imperfect-information games are typically compared using performance metrics on individual games, making it difficult to assess robustness across game choices. Card games are a natural domain for imperfect information due to hidden hands and stochastic draws. To facilitate comparative research on imperfect-information game-playing algorithms and game systems, we introduce Valet, a diverse and comprehensive testbed of 21 traditional imperfect-information card games. These games span multiple genres, cultures, player counts, deck structures, mechanics, winning conditions, and methods of hiding and revealing information. To standardize implementations across systems, we encode the rules of each game in RECYCLE, a card game description language. We empirically characterize each game's branching factor and duration using random simulations, reporting baseline score distributions for a Monte Carlo Tree Search player against random opponents to demonstrate the suitability of Valet as a benchmarking suite.

Problem

• imperfect-information games
• card games
• benchmarking
• algorithm comparison
• testbed

Innovation

• imperfect-information games
• standardized testbed
• card game description language
• Monte Carlo Tree Search
• benchmarking suite