BSc Computer Science with Industrial Experience

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 Exploration & Exploitation, learning through "self-play", Monte Carlo Tree Search and AlphaGo.) 

COMP11120 (for CS students) and COMP13212 are pre-requisites. MATH11121 or equivalent (for CM students).

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. 

ILO 1: Analyze what it means to play a game well by formulating best-response strategies, identifying equilibrium points, and critically evaluating the assumptions and limitations of these solution concepts, including alternative approaches.

ILO 2: Explain the concept of a game by accurately defining its core components, including players, pay-off functions, and rules, and distinguish between normal-form and extensive-form representations.

ILO 3: Explain and assess learning-based approaches to game playing by describing the exploration–exploitation trade-off, explaining learning through self-play, and analyzing how Monte Carlo Tree Search and modern game-playing systems achieve strong performance.

ILO 4: Apply and compare algorithmic methods for finding good game plans by describing the computational complexity of equilibrium computation, implementing or tracing the minimax algorithm with alpha–beta pruning, and explaining the roles of evaluation functions and search procedures in game-playing programs.

ILO 5: Define and interpret strategies as complete plans for decision-making in games, and represent strategies appropriately for different game structures.

Please note: AI forms part of the assessment method for this course unit. This unit looks at using AI techniques in strategic games and has coursework where these techniques are employed (Category D: AI-integrated). For more information, please see: AI in Teaching and Learning Policy. 

Tuition fees for home students commencing their studies in September 2027 will be £10,050 per annum. Tuition fees for international students will be £39,700 per annum.

For general information please see the undergraduate finance pages.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

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