What is Game theory in AI? Nash Equilibrium

Since their roots in game theory and artificial intelligence are similar, these fields have opened up a wide range of research opportunities in recent years. Recent studies have found a strong connection between these two domains and a wide range of applications. Within that context, the researchers underline the many challenges that arise when bridging the gap between them.

The game theory has a real impact when configuring and making plans for an AI model. In actuality, game theory’s many aspects and applications release enormous energy in AI models when linear machine learning intensively deals with a one-dimensional variable.

What is Game Theory?

Game theory is the branch of strategy research that identifies the mathematically and logically sound moves players should make in various games to maximise their benefits.

In 1944, Oskar Morgenstern and John Von Neumann released “The Theory of Games and Economic Behaviour,” which Morgenstern co-authored. This work served as the foundation for game theory as a mathematical theory. But in terms of originality, John Nash, John Harsanyi, and Reinhard Selten, three well-known economists, won the Nobel Prize in Economics in 1994 for their work on game theory in the context of economics.

The word “rational” in this context means that each player regards another player as reasonable and possessing the same degree of information and comprehension as the first player. Each player always favours a greater return or payoff, according to logic.

Each game has the following metrics:

• Rules that aim to police participants’ behaviour
• Rewards like wins, losses, or ties
• methods that influence the decision-making process.

Types of Game Theory

1.    Cooperative and Non-cooperative Games

Participants in cooperative games can establish links through negotiations to increase their chances of winning. In non-cooperative games, they cannot favour support and agreement (e.g. War).

2.    Symmetric and Asymmetric Games

Asymmetric games, in contrast, have players who take into account various goals and incompatible strategies to achieve goals. In symmetric games, each participant has a fixed goal, but their planning, strategies, and actions for achieving goals can only determine who will win the game (for example, Chess).

3.    Perfect and Imperfect Information Games

In games with perfect information, each player can see the moves and decisions of the other players (such as in chess), whereas, in games with imperfect information, each player is blind to the moves and decisions of the other players (e.g. Poker).

4.    Simultaneous and Sequential Games

In contrast to sequential games, where each player is aware of the other players’ moves before them, simultaneous games allow all players to make moves at once (e.g., board games).

5.    Zero-sum and Non-Zero Sum Games

In games with zero-sum outcomes, a player’s gain may result in losses for other players; however, in non-zero-sum outcomes, a player’s gain results in gains for all players.

What is Nash Equilibrium?

Nash equilibrium is the result when no player can raise his reward by changing his decisions; in other words, the player wouldn’t want to modify his decision or action once it has been done since if he did, it would indicate that he was not playing as well as he could.

It is sometimes referred to as the “no regrets” action after the choice. The player won’t look back after weighing the consequences. The Nash equilibrium is not reached under all circumstances but cannot be derailed once it is.

The Nash equilibrium is sometimes known as the “no remorse” condition because it doesn’t. In the game, there may be more than one Nash equilibrium state. This condition typically occurs when a game’s internal variables are more complicated than the two decisions made by two players.

In simultaneous games repeated throughout time, numerous equilibria are eventually found concerning the game types outlined in the section above.

The same situation of several decisions comes up most of the time in the business sector before coming to the final decision, such as ticket fare and eatable goods, for example, when two enterprises are determining the price for enormous replacement products.

AI with Game Theory

Among the games played digitally on laptops, phones, tablets, and other devices, a range of games, like poker, solitaire, chess, ludo, etc., are highly popular. Every game has a defined set of guidelines and rules, and one must follow them to succeed. Game theory is used to create these games digitally; one needs to create algorithms that consider the number of participants and the rules.

How is Game Theory Helping AI?

Game theory is used in Multi-agent Reinforcement Learning to improve traffic flow by deploying AI-controlled Self-Driving vehicles. Imagine that each automobile interacts perfectly with its surrounding environment, but what if the cars thought collectively? Things would get more challenging.

Consider the possibility that an automobile may collide with another vehicle because they may find it more convenient to proceed along a particular route. Game theory is a simple way to model this situation.

In terms of game theory, cars play the role of players, and Nash Equilibrium is where they all work together.

Limitations of Game Theory

If you talk about the problems with the games theory – It counts the concept that agents are rational beings who are self-centred, self-interested, and enormous profit-makers. Games theory has numerous applications based on its functionality, but each invention has its drawbacks.

We consider ourselves social beings who value helping one another and doing so at our sacrifice.

Games theory cannot account for the fact that we occasionally enter Nush Equilibrium and occasionally do not; this completely depends on the social context and other agents.

Final Thoughts

Finally, you must love reading the blog because I assume you are familiar with the basics of game theory. The fundamental concepts of game theory and how AI can use them have been presented.

Machine learning offers several uses for game theory that have an impact on day-to-day activities and real-world applications. Many real-world examples can be used to infer the fundamental idea of game theory. Every day, the choices and acts of others modify the game of life we are playing.