High-level language to build poker agents

Abstract

On the last decade Poker has been one of the most interesting subjects for artificial intelligence, because it is a game that requires game playing agents to deal with an incomplete information and stochastic scenario. The development of Poker agents has seen significant advances but it is still hard to evaluate agents' performance against human players. This is either because it is illicit to use agents in online games, or because human players cannot create agents that play like themselves due to lack of knowledge on computer science and/or AI. The purpose of this work is to fill the gap between poker players and AI in Poker by allowing players without programming skills to build their own agents. To meet this goal, a high-level language of poker concepts - PokerLang - was created, whose structure is easy to read and interpret for domain experts. This language allows for the quick definition of an agent strategy. A graphical application was also created to support the writing of PokerLang strategies. To validate this approach, some Poker players created their agents using the graphical application. Results validated the usability of the application and the language that supports it. Moreover, the created agents showed very good results against agents developed by other experts. © 2013 Springer-Verlag.