Information regarding our 2022/23 admissions cycle

Our 2022/23 undergraduate admissions cycle will open in the week commencing Monday, 18 October. We welcome you to apply now via UCAS, but please note that we will not start reviewing applications until then.

BSc Computer Science and Mathematics / Course details

Year of entry: 2022

Course unit details:
AI and Games

Unit code COMP34111
Credit rating 10
Unit level Level 3
Teaching period(s) Semester 1
Offered by Department of Computer Science
Available as a free choice unit? No

Overview

The main contents of this module include:

 

1. What is a game? (Definition of game, pay-off function, representations in normal form, and extensive form.)

2. What is a plan for decision-making in a game context? (Definition of strategy, representations of strategy.)

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

4. Properties of the Nash equilibrium. (How it incentivizes bad outcomes to prevent opponents from taking advantage.)

5. 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)

6. How do we learn good game plans? (Introduction to reinforcement learning, learning through "self-play", TD-learning, Monte Carlo Tree Search.)

Pre/co-requisites

Unit title Unit code Requirement type Description
Mathematical Techniques for Computer Science COMP11120 Pre-Requisite Compulsory
Data Science COMP13212 Pre-Requisite Compulsory

For students who did not take COMP13212 the pre-requisite is COMP14112

Aims

The aim of the course is to introduce students to the main concepts of non-cooperative game theory and the game solution concept of the Nash equilibrium. Different categories of games and different approaches to effective play in games is developed. During the first six weeks of the course, conceptual and theoretical material is developed. During the final 5 weeks, the students put this material into practice by developing an AI agent which plays a particular game.

Assessment methods

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

Study hours

Scheduled activity hours
Demonstration 3
Lectures 12
Practical classes & workshops 10
Project supervision 5
Independent study hours
Independent study 70

Teaching staff

Staff member Role
Jonathan Shapiro Unit coordinator

Return to course details