This course provides a comprehensive introduction to Artificial Intelligence, covering both foundational concepts and key algorithmic techniques. It begins with an overview of AI and problem-solving using single-agent search methods such as A* search. The course then explores decision-making in adversarial settings through two-agent search techniques, including minimax and alpha-beta pruning.
Building on these foundations, the course introduces sequential decision-making under uncertainty via Markov Decision Processes (MDPs). It further covers learning-based approaches such as Multi-Armed Bandits (MAB) and Reinforcement Learning (RL).
Finally, the course examines strategic interactions in multi-agent systems through game theory and multi-agent reinforcement learning. It focuses on decision-making in competitive and cooperative environments, analyzing how agents’ choices impact outcomes and how equilibrium concepts can guide optimal strategies. Applications span economics, social sciences, and modern AI systems, providing a framework for understanding complex decentralized interactions and designing efficient algorithms.