As a classic example of imperfect information games, Heads-Up No-limit Texas Holdem (HUNL), has been studied extensively in recent years. While state-of-the-art approaches based on Nash equilibrium have been successful, they lack the ability to model and exploit opponents effectively. This paper presents an evolutionary approach to discover opponent models based Long Short Term Memory neural networks and on Pattern Recognition Trees. Experimental results showed that poker agents built in this method can adapt to opponents they have never seen in training and exploit weak strategies far more effectively than Slumbot 2017, one of the cutting-edge Nash-equilibrium-based poker agents. In addition, agents evolved through playing against relatively weak rule-based opponents tied statistically with Slumbot in heads-up matches. Thus, the proposed approach is a promising new direction for building high-performance adaptive agents in HUNL and other imperfect information games.

Now entering its eighth year, the Annual Computer Poker Competition (ACPC) is the premier event within the field of computer poker. With both academic and nonacademic competitors from around the world, the competition provides an open and international venue for benchmarking computer poker agents. We describe the competition's origins and evolution, current events, and winning techniques The competition has been held annually since 2006, open to all competitors, in conjunction with top-tier artificial intelligence conferences (AAAI and IJCAI). In 2006 the competition began with only 5 competitors. Since then, the total number of competitors has increased.

The game of poker has been identified as a beneficial domain for current AI research because of the properties it possesses such as the need to deal with hidden information and stochasticity. The identification of poker as a useful research domain has inevitably resulted in increased attention from academic researchers who have pursued many separate avenues of research in the area of computer poker. The poker domain has often featured in previous review papers that focus on games in general, however a comprehensive review paper with a specific focus on computer poker has so far been lacking in the literature. In this paper, we present a review of recent algorithms and approaches in the area of computer poker, along with a survey of the autonomous poker agents that have resulted from this research. We begin with the first serious attempts to create strong computerised poker players by constructing knowledge-based and simulation-based systems.

Self-play reinforcement learning has proved to be successful in many perfect information two-player games. However, research carrying over its theoretical guarantees and practical success to games of imperfect information has been lacking. In this paper, we evaluate self-play Monte-Carlo Tree Search in limit Texas Hold'em and Kuhn poker. We introduce a variant of the established UCB algorithm and provide first empirical results demonstrating its ability to find approximate Nash equilibria.

Computer Poker's unique characteristics present a well-suited challenge for research in artificial intelligence. For that reason, and due to the Poker's market increase in popularity in Portugal since 2008, several members of LIACC have researched in this field. Several works were published as papers and master theses and more recently a member of LIACC engaged on a research in this area as a Ph.D. thesis in order to develop a more extensive and in-depth work. This paper describes the existing research in LIACC about Computer Poker, with special emphasis on the completed master's theses and plans for future work. This paper means to present a summary of the lab's work to the research community in order to encourage the exchange of ideas with other labs / individuals. LIACC hopes this will improve research in this area so as to reach the goal of creating an agent that surpasses the best human players.