How is Game Theory and AI Related?
Game theory is defined as the mathematics that is used to determine the outcome of the game based on the strategies employed by different players. According to this theory, each player tries to set strategies that can help them earn the maximum rewards. Game theory can be used in artificial intelligence in different ways. To understand the connection between game theory and artificial intelligence, you must know the 5 main types of games.
Types of Games
1. Cooperative and Non-cooperative Games: Each player can form an alliance with other members to improve their chances of winning the best reward. This is called the cooperative games where two or more parties form an alliance to win the game. Non-cooperative games, on the other hand, are those where it is not possible to negotiate.
2. Symmetric and Asymmetric Games: As the names imply, symmetric games are those where each participant plays to achieve the same objective, but applies different strategies to defeat their opponent. The chest is the best example of symmetric games. In asymmetric games, the players have varying goals.
3. Perfect and Imperfect Games: Perfect games are the ones where each participant can plan their strategies based on the opponent’s move. In other words, they get to see the other participant’s move, for example, chess. Imperfect games keep the moves of each player hidden, for example, cards.
4. Simultaneous and Sequential Games: Simultaneous games allow players to take actions at the same time, however, sequential games give each player an opportunity to monitor the moves of other players before taking an action.
5. Zero Sum and Non-Zero Sum Games: Zero-sum games result in a loss for a player when another player wins. Non-zero sum games are the ones where many players benefit from the winning of another player.
Connection Between AI and Game Theory
Note that the game theory isn’t only applied to the games. It rather has an extensive range of applications, including Generative Adversarial Networks and Machine Learning. One common property that both AI and game theory share is the Minimax Algorithm. This algorithm of Game Theory happens to be one of the traditional and most commonly used algorithms in Artificial Intelligence.
In the Nash Equilibrium, each participant sets different strategies for the game. However, the results will be in favor of each player only if they stick to their original strategy, even when they know the strategy of another participant. Many aspects of this theory are related to Machine Learning and Artificial Intelligence. Most of the digital games are developed with a combination of AI and game theory. When a bunch of people are competing for the reward, then the decision of each participant will affect the strategy of another one. In other words, the choice of one player has a big impact on the other player. That’s when the game theory is used in the AI. Game theory is often applied to artificial intelligence when two or more people are competing to solve a logical problem.