What is Game Theory in AI?

Game Theory is applicable to several areas of artificial intelligence, including: Mathematical game theory is used to simulate how various players would interact strategically in a setting with predetermined rules and consequences. Different areas of artificial intelligence can benefit from the application of game theory:

1. Multi-agent AI systems.
2. Imitation and Reinforcement Learning.
3. Adversary training in Generative Adversarial Networks (GANs).

In addition, machine learning models and many events in daily life may be described using game theory. For instance, a two-person game in which one player challenges the other to locate the best hyper-plane providing him the most demanding points to classify may be used to demonstrate a classification technique like SVM (Support Vector Machines). The outcome of the game will then condense into a trade-off between the two players’ strategic prowess (eg. how well the fist player was challenging the second one to classify difficult data points and how good was the second player to identify the best decision boundary).

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Gaming Theory

There are five primary categories of games in game theory:

1. Games that are cooperative vs non-cooperative allow players to form alliances to increase their chances of winning (eg. negotiations). Instead of forming alliances, players cannot do so in non-cooperative games (eg. wars).
2. Games can be either symmetric or asymmetric. In a symmetric game, everyone has the same objectives, and the winner is determined only by the techniques each player uses to attain them (eg. chess). Instead, in asymmetric games, players have opposing or discordant objectives.
3. Games with Perfect Information vs. Games with Imprecise Information: In Perfect Information games, all participants can observe each other’s movements (eg. chess). Instead, the actions of other players are concealed in games with imperfect information (eg. card games).
4. Games that are simultaneous vs those that are sequential: In simultaneous games, many players can act at once. Instead, in sequential games, every player is informed of the prior deeds of every other player (eg. board games).
5. Games that are Zero-Sum versus Non-Zero-Sum: In Zero-Sum games, if one player gets anything, the other players lose. Instead, many players might profit from one another’s gains in non-zero sum games.

The Nash Equilibrium

The Nash Equilibrium is a state in which every participant in the game acknowledges that their current position is the best one possible for the game at this time. No one’s benefit would come from altering their existing approach (based on the decisions made by the other players).

Inverse Game Theory

Game theory seeks to comprehend a game’s dynamics in order to maximise participants’ potential outcomes. Instead, inverse game theory seeks to create a game based on the objectives and strategies of the players. Designing the surroundings for AI Agents involves a significant amount of inverse game theory.

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Generative Adversarial Networks’ Adversarial Training (GANS)

Two separate models make up GANs: a generating model and a discriminative model.

Generative models take some attributes as input, make analysis of their distributions, and attempt to comprehend how they were created. Restricted Boltzmann Machines (RBMs) and Hidden Markov Models (HMMs) are two types of generative models (RBMs).

Instead, discriminative models use the input attributes to suggest a potential class to which our sample could belong. A discriminative model is an example of this, such as Support Vector Machines (SVM). This processing procedure closely mirrors the game’s dynamics. Our players — the two models — are competing against one another in this game. While the second player attempts to grow better and better at recognising the appropriate samples, the first player makes bogus samples to deceive the other.

The learning settings are then modified after each repetition of the game in order to lower the total loss.

Reward-Based Multi-Agent Learning (MARL)

Reinforcement learning (RL) seeks to enable learning through contact with the environment for an agent (our “model”) (this can be either virtual or real). In the beginning, RL was created to follow Markov Decision Processes. In this context, a stochastic stationary environment is used to situate an agent while it tries to learn a policy via a reward/punishment mechanism. It is demonstrated in this case that the agent will eventually reach an acceptable policy.

However, this condition is no longer valid if more than one agent is present in the same environment. In fact, whereas before the agent’s learning depended only on interactions with its surroundings, it now also depends on interactions with other agents.

Imagine if a fleet of AI-powered self-driving cars is being used to help a city’s traffic flow. Each of the vehicles can interact with the world outside correctly while acting alone, but if we want to teach the cars to think collectively, things might get trickier. For instance, a car and another automobile could collide because it is most practical for both of them to travel a particular path.

Game Theory may be used to model this situation with ease. In this scenario, the various participants would be represented by our automobiles, and the Nash equilibrium would be the equilibrium result of the interaction between the various cars.

It may be quite challenging to model systems that include a lot of agents. This is due to the fact that as the number of agents increases, so does the number of potential interactions between the various agents.

Modeling Multi-Agent Reinforcement Learning Models with Mean Field Scenarios (MFS) may be the best course of action in these situations. In reality, by supposing that all agents have comparable reward functions, Mean Field Scenarios can simplify the complexity of MARL models. Several reputed institutes now offer the machine learning online course as well.