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This is an archived syllabus from 2013-2014

COMP34120 AI and Games syllabus 2013-2014

COMP34120 AI and Games

Level 3
Credits: 20
Enrolled students: 45

Course leader: Jonathan Shapiro

Additional staff: view all staff


  • Pre-Requisite (Compulsory): COMP14112
  • Pre-Requisite (Compulsory): COMP11120

Additional requirements

  • Pre-requisites are waived for CM students.

Assessment methods

  • 40% Written exam
  • 60% Coursework
Sem 1 w1-5,7 Lecture LF15 Thu 10:00 - 10:00 -
Sem 1 w1-5,7 Lecture LF15 Tue 12:00 - 12:00 -
Sem 1 w8-12 Workshop Collab 2 Thu 10:00 - 10:00 -
Sem 1 w8-12 Workshop Collab Tue 12:00 - 12:00 -
Sem 2 w19-24 Lecture LF15 Tue 09:00 - 09:00 -
Sem 2 w19-24 Lecture LF15 Mon 16:00 - 16:00 -
Sem 2 w25-28,32-33 Workshop Collab Tue 09:00 - 09:00 -
Sem 2 w25-28,32-33 Workshop Collab Mon 16:00 - 16:00 -
Themes to which this unit belongs
  • Learning and Search in Artificial Intelligence


Games have become very successful as a way of modelling the interactions of multiple agents. In this course unit we will look at the formal theory of games, which includes a discussion of what it might to find a solution to a game, and then look at how to program computers to play games, with a particular emphasis of programs that are capable of learning.

This is a 20 credit course unit that runs for the entire year. Each semester is structured as follows:

There are 12 lectures in the first six weeks of term to introduce the material, supported by handouts. In the remaining 5 weeks of terms students work, in small groups, on writing a program that implements some of the techniques taught previously. There is one exam in the May/June exam period.

The lectures are designed to support the handouts and it is expected that students read these both, in preparation for a lecture and again afterwards to ensure they have understood the material, which is quite abstract in places.

The first lecture will go into more detail regarding organizational issues and students thinking about taking this course unit are strongly encouraged to attend.


Semester 1:

The aim of this semester is to answer the following questions.

What is a game? (Definition of game tree, pay-off function, normal form, extensive form.)

How can we describe a game plan? (Definition of strategy, representations of same.)

What does it mean to play a game well? (Definition of best-response strategy, equilibrium point and similar, discussion of the validity of these concepts, discussion of alternatives.)

How do we find good game plans? (Complexity of finding equilibrium points, minimax algorithm, alpha-beta pruning, discussion of the components of a typical game playing program via evaluation function and alpha-beta search).

What are some of the processes that can be modelled using games?

Semester 2:

The aim of this semester is to understand the following topics.

What are Stackelberg (Leader-Follower) games? What constitutes a solution to a Stackelberg game? How can learning and optimization be used to learn good solutions? Applications to price setting and marketing will be discussed.

What is reinforcement learning and how is it applied to on-line learning? What are the important mechanisms of on-line learning? How can reinforcement learning be applied to games situations?

Feedback methods

Feedback is provided is several ways. Half of the course involves
group projects which take place in a drop-in lab where the students
can work, and get feedback on what they are doing. For each project,
each group must make a presentation, and feedback is provided
verbally. In addition, written feedback is provided.

Study hours

  • Assessment written exam (2 hours)
  • Lectures (24 hours)
  • Practical classes & workshops (26 hours)

Employability skills

  • Analytical skills
  • Group/team working
  • Innovation/creativity
  • Project management
  • Oral communication
  • Problem solving
  • Research
  • Other

Learning outcomes

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

Learning outcomes are detailed on the COMP34120 course unit syllabus page on the School of Computer Science's website for current students.

Reading list

No reading list found for COMP34120.

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.