Course Details for A.Y. 2018/2019

The information on this page refer to the academic year 2018/2019. Therefore, several links are not available and data may be incomplete. To read the current information on the course, if it is still available, go to the university course catalogue . To read information relating other academic years, use the list at the bottom of this page.

A. Year:

2018/2019

Name:

Non-cooperative networks / Non-cooperative networks

Type:

Module

Main course:

Autonomous Networks / Autonomous Networks

Basic information

Code:

DT0174

Sector:

INF/01

Credits:

: Master Degree in Computer Science 3 CFU (c)

Term:

1^st semester

Degree(s):

Master Degree in Computer Science 2^nd anno curriculum NEDAS Compulsory
Master Degree in Computer Science 2^nd anno curriculum SEAS Elective
Master Degree in Computer Science 2^nd anno curriculum UBIDIS Elective

Language:

English

Teacher(s):

Guido Proietti

Course Objectives

The course is focused on the algorithmic aspects of non-cooperative networks, where selfish agents own network components and influence through their rational but self-interested behaviour the quality of implemented solution. The set of topics analyzed ranges from the study of the equilibria space of classic network games, up to algorithmic mechanism design for classic network optimization problems.

Course Content

Strategic equilibria theory in Non-Cooperative Networks (NCN): Nash equilibria and dominant stratgy equlibria. Selfish routing, Network Design games, Network Creation games.
Implementation theory in NCN: Algorithmic mechanism design (AMD). AMD for some basic graph optimization problems: Shortest path, Minimum Spanning Tree, Shortest-path Tree

Learning Outcomes (Dublin Descriptors)

On successful completion of this course, the student should

By the end of this module students will be able to understand the difference between a canonical and a strategic distributed system.
The aim is to make the student capable of abstracting models and formal algorithmic problems from real (i.e. non-cooperative) distributed computational problems, and designing efficient algorithmic solutions.
Through the presentation and the comparison of different solutions to a given probelm, students will be guided to learn and to identify independently their most efficient solution.
The course will encourage the development of the following skills of the student: capability of formally presenting and modelling concrete problems, focusing on their main features and discarding the inessential ones.
The course aims to develop in graduate students competencies and abilities necessary in their future studies, especially with respect to doctoral studies on algorithmic topics.

Prerequisites and Learning Activities

Knowledge of basic courses of discrete mathematcs and algorithms.

Assessment Methods and Criteria

Mid-term written examination, followed by a final oral examination, which, for those who performed successfully in the mid-term examination, will be restricted to the second part of the course.

Textbooks

Noam Nisan, Tim Roughgarden, Eva Tardos, and Vijay V. Vazirani, Algorithmic Game Theory , Cambridge University Press. 2011.

Internet Resources

Homepage:

http://www.di.univaq.it/~proietti/didattica.html

Course page updates

This course page is available (with possible updates) also for the following academic years:

To read the current information on this course, if it is still available, go to the university course catalogue .

Course information last updated on: 20 ottobre 2016, 13:54