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COMP34612 Computational Game Theory syllabus 2021-2022

COMP34612 Computational Game Theory

Level 3
Credits: 10
Enrolled students: pending

Course leader: Xiao-Jun Zeng


Additional staff: view all staff

Requisites

  • Pre-Requisite (Compulsory): MATH10111
  • Pre-Requisite (Compulsory): COMP11120
  • Pre-Requisite (Compulsory): COMP13212
  • Pre-Requisite (Optional): COMP24112
  • Co-Requisite (Optional): COMP34111

Assessment methods

  • 50% Written exam
  • 50% Coursework
Timetable
SemesterEventLocationDayTimeGroup
Sem 2 w20-25 Examples Simon TH A Tue 12:00 - 13:00 -
Sem 2 w20-25 Lecture Simon TH A Mon 14:00 - 15:00 -
Sem 2 w20-25 Lecture Simon TH A Thu 16:00 - 17:00 -
Sem 2 w26-27,31-33 Workshop Simon TH A Tue 12:00 - 13:00 -
Sem 2 w26-27,31-33 Workshop Simon TH A Thu 16:00 - 17:00 -

Overview

There has been a substantial grow of research activity at the boundaries of game theory, artificial intelligence, economics, computer science, and a number of other disciplines in recent years. The reasons behind this are twofold: On the one hand, game theory and its applications raise many important and challenging computing, learning, and communication problems to CS and AI; On the other hand, game theory provides important insights and powerful frameworks to a number of CS topics, including AI, Multi-agent systems, computer networks as well as many others.

The main contents of this module include:

1) To introduce the concepts and computational solutions for non-cooperative and cooperative game theory with their applications

2) To introduce the machine learning techniques to solve the learning issues arise from the applications of game theory with their applications

3) To introduce the mechanism design (the reverse game theory) and its applications for the design of the rules of a game

The module includes a major piece of coursework (a group project run over 5 weeks) to apply game theory and learning methods covered to solve the pricing game problem.

Aims

This module teaches the fundamental concepts of game theory and their computational methods to enable students to master the concepts/tools from game theory to model/analyse the interaction agents/systems, and to build skills in machine learning and optimisation methods for game analysis and problem solving.

Study hours

  • Demonstration (6 hours)
  • Lectures (12 hours)
  • Practical classes & workshops (10 hours)

Learning outcomes

On successful completion of this unit, a student will be able to:

Reading list

No reading list found for COMP34612.