Multiagent Systems, Distributed Artificial Intelligence, Game Theory, Decision Making or Reasoning under Uncertainty, Coordination, Knowledge and Information, Mechanism Design, Reinforcement Learning.

MAS is an expanding field that blends classical areas like game theory and

decentralized control with modern fields like computer science and machine learning.

This 7-lecture course provides us a concise introduction to the subject, covering the theoretical foundations as well as more recent developments.

Note: An intelligent agent is a decision maker or reasoner or problem solver.

Lecture 1 is a short introduction to the field of multiagent systems. Lecture 2 covers the basic theory of single-agent decision making under uncertainty.

Lecture 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium.

Lecture 4 deals with the fundamental problem of coordinating a team of collaborative agents.

Lecture 5 studies the problem of multiagent reasoning and decision making under partial observability.

Lecture 6 focuses on the design of protocols that are stable against manipulations by self-interested agents.

Lecture 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning.