BSc Computer Science with Industrial Experience / Course details

Year of entry: 2022

Course unit details:
Computational Game Theory

Unit code COMP34612
Credit rating 10
Unit level Level 3
Teaching period(s) Semester 2
Offered by Department of Computer Science
Available as a free choice unit? No


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.


Unit title Unit code Requirement type Description
Foundations of Pure Mathematics B MATH10111 Pre-Requisite Compulsory
Mathematical Techniques for Computer Science COMP11120 Pre-Requisite Compulsory
Data Science COMP13212 Pre-Requisite Compulsory
Machine Learning COMP24112 Pre-Requisite Optional
AI and Games COMP34111 Co-Requisite Optional

For Computer Science and Maths students the pre-requisite is MATH10111. For Single Honours students the pre-requisite is COMP11120


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.

Learning outcomes



Assessment methods

Method Weight
Written exam 50%
Written assignment (inc essay) 50%

Study hours

Scheduled activity hours
Demonstration 6
Lectures 12
Practical classes & workshops 10
Independent study hours
Independent study 72

Teaching staff

Staff member Role
Xiaojun Zeng Unit coordinator

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