Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Artificial Intelligence - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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1 course among the following2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud, Visual intelligence, Statistical learning - 1st and 2nd year: Computer Vision & Deep Learning)2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud - 1st and 2nd year: Computer Vision & Deep learning)2 courses among the following (A.A. 2023/24: Complex systems and Network Science not activated)Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Computational Game Theory (2023/2024)
Teaching code
4S010687
Teacher
Coordinator
Credits
6
Also offered in courses:
- Algorithmic Game Theory of the course Master's degree in Computer Science and Engineering
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
Semester 2 dal Mar 4, 2024 al Jun 14, 2024.
Courses Single
Authorized
Learning objectives
Many problems in computer science involve settings where multiple self-interested parties interact, e.g., resource allocation in large networks, online advertising, managing electronic marketplaces and networked computer systems. Computational (algorithmic) game theory complements economic models and solution concepts, to reason about how agents should act when the actions of other agents affect their utilities, with a focus to discuss computational complexity issues, and the use of approximation bounds for models where exact solutions are unrealistic. The course aims to give students an introduction to the main concepts in the field of computational game theory with representative models and (algorithmic) solution chosen to illustrate broader themes. Students will acquire the basic skills to design models and computer systems that performs optimally/well in some paradigmatic multiagent settings; and to reason about the design of mechanisms to incentivate self-interested users to behave in a desirable way.
Examination methods
The exam verifies that the students have acquired sufficient confidence and skill in the application of the basic game thoretic models and their solutions, and are able to contextualize them in novel multiagent scenarios.
The exam consists of a written test with open questions and multiple choice questions. The test includes some mandatory exercises and a set of exercises among which the student can choose what to work on. The mandatory exercises are meant to verify a straightforward application of the elements studied in class. The "free-choice" exercises test the ability of students to re-elaborate these notions in "new" scenarios.
Prerequisites and basic notions
Basic knowledge of discrete maths and calculus
Basic probability theory
Program
1. Introduction to strategic games, costs, payoffs; basic solution concepts; equilibria and learning in games; Nash equilibrium; repeated games; cooperative games. 2. Basic computational issues of finding equilibrium. 3. Repeatedly making decisions with uncertainty; learning, regret minimization and equilibrium. 3. Graphical games and connections to probabilistic inference in machine learning. 4. Elements of Mechanism Design; Auctions; distributed mechanism design.
Bibliography
Didactic methods
The course is ogranized around 2 weekly lectures, homeworks assigned to clarify and deepen on the theory topics and some exercise sessions, where the solution to these exercises will be discussed.
Learning assessment procedures
The exam consists of a written test with open questions and multiple choice questions. The test includes some mandatory exercises and a set of exercises among which the student can choose what to work on. The mandatory exercises are meant to verify a straightforward application of the elements studied in class. The "free-choice" exercises test the ability of students to re-elaborate these notions in "new" scenarios.
Depending on the number of students attending, the exam can be partially based on the discussion of a scientific article on the application of computational game theory
Evaluation criteria
The exam verifies that the students have acquired sufficient confidence and skill in the application of the basic game thoretic models and their solutions, and are able to contextualize them in novel multiagent scenarios.
Criteria for the composition of the final grade
In the case the exam includes two parts (e.g., written test and oral discussion of an article) the final grade will be computed by averaging the grades awarded to the two parts of the exam.
Exam language
english (italiano per studenti di LM in italiano)
