Details
- Time & location
-
Lectures: Tu, W 11:30 - 12:50, E7 4433
- Teaching team
-
- Description
- This course is an introduction to the mathematical and computational foundations of modern multi-agent systems, with a focus on game theory, artificial intelligence, and machine learning. The course provides analytical tools to analyze and model multi-agent systems in which an agent's welfare is a function of not only their own actions, but also those of others. Tentative topics include normal-form games, extensive-form games, repeated games, stochastic games, Bayesian games, computation of solution concepts, and learning in multi-agent systems.
- Prereq
- There are no formal prerequisites, and it is assumed that most students in the class will be unfamiliar with game theory, mechanism design, and multi-agent systems. Since some of the material is quite mathematically formal, students are expected to have introductory knowledge in probability theory (ECE203 or equivalents), computational complexity (ECE208 or equivalents), and algorithms (ECE250 or equivalents).
- Antireq
- Students will not be allowed to take this course if they have completed (or are currently taking) ECON412, CO456, CO759 (Algorithmic Game Theory), CS886 (Multi-agent Systems), and MSCI724 (Game Theory and Recent App)
- Readings
-
Main textbook
- Multiagent Systems: Algorithmic, Game-theoretic, and Logical Foundations (freely available online)
Optional references
- Algorithmic Game Theory (freely available online)
- Twenty Lectures on Algorithmic Game Theory (freely available online)
- The Theory of Learning in Games
- Game Theory
- A Course in Game Theory (freely available online)
- Evaluation
-
The course has two different grading schemes are as follows.
- Undergraduate: 5% quizzes; 20% assignments; 25% (survey) project; 50% final exam
- Graduate: 5% quizzes; 20% assignment; 45% (research) project; 30% final exam
Quizzes, assignments, and the final exam are the same for both schemes but have different percentage breakdowns in each scheme. The project, however, has different requirements in each scheme (see "Policies" for more details). Undergraduate students have the option of choosing the graduate grading scheme when they submit their project proposal. We reserve the right to make changes to the exact percentage breakdowns above.
- Contingency proviso
- We are facing unusual and challenging times. The course outline presents the instructor’s intentions for course assessments, their weights, and due dates in Winter 2024. As best as possible, we will keep to the specified assessments, weights, and dates. To provide contingency for unforeseen circumstances, the instructor reserves the right to modify course topics and/or assessments and/or weight and/or deadlines with due and fair notice to students. In the event of such challenges, the instructor will work with the Department/Faculty to find reasonable and fair solutions that respect rights and workloads of students, staff, and faculty.
- Main Topics (tentative)
-
The course tentatively covers the following topics:
-
Introduction to multi-agent systems
Overview of game theory and mechanism design, rationality and self-interest, utility theorem, risk attitudes
-
Normal-form games
Pure and mixed-strategy Nash equilibrium, iterative elimination of dominated strategies, price of anarchy, correlated equilibrium, computing solution concepts of normal-form games
-
Extensive-form games
Perfect and imperfect-information games, finite and infinite-horizon games, subgame-perfect equilibrium, backward induction, one-shot deviation principle
-
Beyond normal and extensive-form games
Repeated games, stochastic games, Bayesian games, congestion games, trigger strategies, folk theorems, Bayes-Nash equilibrium, auctions, optimal auctions, revenue-equivalence theorem, incentive compatibility, VCG mechanisms
-
Learning in multi-agent systems
Multi-agent reinforcement learning, fictitious play, Bayesian learning, regret-minimization learning
-