Game Theory in AI

Game theory is a field of mathematics and computer science that offers a formal description of strategic interactions between agents. In the context of artificial intelligence, game theory provides powerful frameworks for understanding multi-agent systems, decision-making under uncertainty, and competitive scenarios.

Key Concepts

Nash Equilibrium

A fundamental concept where no player can benefit from unilaterally changing their strategy. This has profound implications for designing AI systems that interact with other rational agents.

Cooperative vs. Non-Cooperative Games

Understanding when AI agents should cooperate versus compete is crucial for building systems that can work in multi-agent environments effectively.

Applications in AI

1. Multi-Agent Reinforcement Learning: Game theoretic concepts help design reward structures and learning algorithms for agents in competitive settings.

2. Auction Mechanisms: AI-powered bidding systems in advertising and resource allocation rely heavily on game theoretic principles.

3. Security Applications: Adversarial machine learning and cybersecurity benefit from understanding attacker-defender dynamics.

Future Directions

The intersection of game theory and deep learning continues to produce exciting research, particularly in areas like:

• Generative Adversarial Networks (GANs)
• Multi-agent cooperation and competition
• Mechanism design for AI systems

This research was conducted as part of my studies at Mohammed V University of Rabat.