Esta ponencia forma parte de : 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020)Tabletop games come in a variety of forms, including board
games, card games, and dice games. In recent years, their
complexity has considerably increased, with many components, rules that change dynamically through the game, diverse
player roles, and a series of control parameters that influence
a game’s balance. As such, they also encompass novel and
intricate challenges for Artificial Intelligence methods, yet research largely focuses on classical board games such as chess
and Go. We introduce in this work the Tabletop Games (TAG)
framework, which promotes research into general AI in modern tabletop games, facilitating the implementation of new
games and AI players, while providing analytics to capture the
complexities of the challenges proposed. We include preliminary results with sample AI players, showing some moderate
success, with plenty of room for improvement, and discuss
further developments and new research direction
