In recent years there has been a great deal of progress in computational methods for solving large games. Interest in the subject stems from both practical applications where AIs, such as self-driving vehicles, interact with each other and humans, and from a handful recreational games, such as chess, poker and Go, that are seen as challenging surrogates for real-world applications, while simultaneously appealing to a large population of devoted enthusiasts. In particular, work on the popular variant of poker known as Texas Hold'em has seen many years of progress culminate in a number of high-profile success stories. Poker and other card games are especially challenging, as they are games with imperfect information and a large number of game states.

Game Theory is a branch of mathematics used to model the strategic interaction between different players in a context with predefined rules and outcomes. Game Theory can also be used to describe many situations in our daily life and Machine Learning models (Figure 1). For example, a Classification algorithm such as SVM (Support Vector Machines) can be explained in terms of a two-player game in which one player is challenging the other to find the best hyper-plane giving him the most difficult points to classify. The game will then converge to a solution which will be a trade-off between the strategic abilities of the two players (eg. Different aspects of Game Theory are commonly used in Artificial Intelligence, I will now introduce you to the Nash Equilibrium, Inverse Game Theory and give you some practical examples.

Game theory is one of the most relevant aspects in modern multi-agent artificial intelligent(AI) systems. To some extent, the recent evolution of AI has triggered a renaissance in the field of game theory fostering innovation across all sorts of new areas. One of those areas is the field of asymmetric games that describe settings in which different players can follow different strategies. Last year, Alphabet's subsidiary DeepMind published a super innovative way to tackle asymmetric game problems. DeepMind's breakthrough can have profound implications in modern multi-agent, AI systems that are often modeled as asymmetric games.