Valet: A Standardized Testbed of Traditional Imperfect-Information Card Games
This provides a standardized testbed for researchers to compare imperfect-information game-playing algorithms, though it is incremental as it focuses on benchmarking rather than new AI methods.
The authors tackled the lack of standardized benchmarks for AI algorithms in imperfect-information games by introducing Valet, a testbed of 21 traditional card games, and reported baseline score distributions from simulations to demonstrate its suitability for benchmarking.
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.