In many imperfect information games, the ability to exploit the opponent is crucial for achieving high performance. For instance, skilled poker players usually capitalize on various weaknesses in their opponents’ playing patterns and styles to maximize their earnings. Therefore, it is important to enable computer players in such games to identify flaws in opponent strategies and adapt their behaviors to exploit these flaws. This paper presents a genetic algorithm to evolve adaptive LSTM (Long Short Term Memory) poker players featuring effective opponent exploitation. Experimental results in heads-up no-limit Texas Hold’em demonstrate that adaptive LSTM players are able to obtain 40% to 1360% more earnings than cutting-edge game theoretic poker players against opponents with various flawed strategies. In addition, experimental results indicate that adaptive LSTM players evolved through playing against simple and weak rule-based opponents can achieve comparable performance against top game-theoretic poker players. The approach introduced in this paper is a promising start for building adaptive computer players for imperfect information games.

Doug Polk is a professional poker player who's won millions of dollars, mostly at heads-up Texas Hold Em No Limit. He was part of a team that recently beat an artificial intelligence bot programmed by MIT students. When they're not at the tables collecting cash, Polk and fellow poker pro Ryan Fee run Upswing Poker, a site that offers training to everyday players who want to improve. Since so much Wall Street trading is conducted by computer programs these days, I figured he might have some wisdom about going up against AI for the few remaining humans who discretionary trade. John Navin: Like many human, discretionary traders on Wall Street, you've gone up against an artificial intelligence bot.

New AI vs. humans poker challenge is set to kick off on Wednesday. The poker-playing algorithm humans will be facing this time around was named Libratus. After successfully solving games like chess and even Go, scientists seem bent to crack poker next. The research into an artificial intelligence that could stand up to top-tier poker players has been going on for some time now. The results so far haven't been very encouraging for these scientists, but they aren't giving up.

John McCarthy coined the term'artificial intelligence' in 1955, describing the field as "the science and engineering of making intelligent machines". Back then, many of the first applications of the early computers were AI programs. In 1956, Allen Newell and Herbet A. Simon created Logic Theorist, a program that discovered proofs in propositional logic. Another example is the software built to play checkers by Arthur Samuel. While most of these programs focused on search and learning as the foundation of the newly discovered field, the tricky part was getting AI to solve problems – and AI has gotten pretty good at it over the years.

I would like to see more work done on no limit hold'em, as I think solving that problem will offer practical insights into how one can effectively manage risk under incomplete information. My (perhaps incorrect) understanding is that only limit hold'em, played heads up, has been "solved", and it was done in a brute forced kind of way. Would like to read more about possible research direction with regards to no-limit hold'em if anyone has any interesting links. It is possible that no only professional poker players' jobs will get disrupted, but also many professional financial investment "professionals" may lose their jobs once no limit hold'em gets worked out.

Computing a good strategy in a large extensive form game often demands an extraordinary amount of computer memory, necessitating the use of abstraction to reduce the game size. Typically, strategies from abstract games perform better in the real game as the granularity of abstraction is increased. This paper investigates two techniques for stitching a base strategy in a coarse abstraction of the full game tree, to expert strategies in fine abstractions of smaller subtrees. We provide a general framework for creating static experts, an approach that generalizes some previous strategy stitching efforts. In addition, we show that static experts can create strong agents for both 2-player and 3-player Leduc and Limit Texas Hold'em poker, and that a specific class of static experts can be preferred among a number of alternatives. Furthermore, we describe a poker agent that used static experts and won the 3-player events of the 2010 Annual Computer Poker Competition.

One approach for artificially intelligent agents wishing to maximise some performance metric in a given domain is to learn from a collection of training data that consists of actions or decisions made by some expert, in an attempt to imitate that expert's style. We refer to this type of agent as an expert imitator. In this paper we investigate whether performance can be improved by combining decisions from multiple expert imitators. In particular, we investigate two existing approaches for combining decisions. The first approach combines decisions by employing ensemble voting between multiple expert imitators. The second approach dynamically selects the best imitator to use at runtime given the performance of the imitators in the current environment. We investigate these approaches in the domain of computer poker. In particular, we create expert imitators for limit and no limit Texas Hold'em and determine whether their performance can be improved by combining their decisions using the two approaches listed above.