Valet: A Standardized Testbed of Traditional Imperfect-Information Card Games
2026-03-03 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created Valet, a collection of 21 different card games that involve hidden information and chance to help test AI strategies across many game types. They made a special language called RECYCLE to clearly describe the rules of all these games so different AI systems can use the same setup. They ran tests to understand how complex and long each game can be and provided example results using one common AI method. This helps researchers compare AI performance consistently across many imperfect-information card games.
imperfect-information gamescard gamesMonte Carlo Tree Searchbranching factorgame durationhidden informationRECYCLE languagebenchmarkingstochastic drawsAI algorithms
Authors
Mark Goadrich, Achille Morenville, Éric Piette
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.