This module will enable students to gain a deeper understanding of the bespoke game AI problems found in commercially available video games. These problems include:

Navigating a car around a track
Traffic Systems
Agent Formation Movement
Squad Behaviour
Navigation through a busy environment of hundreds of agents
Target Selection
Cover Systems
Threat Analysis
AI Awareness
Tactical Positioning
Tethering

Assessment Component 1 – Game AI Architecture Solution 60% [Learning outcomes 1, 2 and 3]

This assessment requires students to develop a practical solution to a pre-selected complex game AI problem that demonstrates their understanding of AI architectures. The solution should follow industry standard principles in terms of efficiency, balance and creativeness. The artefact will be able to demonstrate student understanding of advanced AI architectures.

Assessment Component 2 – Literature Review 40% [Learning outcomes: 1, 3 and 4]

A written literature review of contemporary AI architecture literature used to provide solutions to the given problem solved in Assessment Component 1. This review will demonstrate an understanding of the variety and effectiveness of approaches by which the given problem can be solved.

Learning and teaching activities will be delivered through a structured blend of scheduled and independent study designed to support a coherent learning journey. Scheduled sessions will typically include lectures that introduce core concepts and workshops that allow students to apply techniques, engage in facilitated discussions, and undertake activities focused on problem solving and peer learning. Independent study will involve, recommended reading, research tasks, and ongoing development of project work supported by the resources provided.

1. Demonstrate a thorough understanding of game AI architectures and concepts.

Knowledge & understanding

2. Critically evaluate and compare the potential solutions to construct a working solution to a complex problem.

Application & problem-solving
Digital Literacy

3. Conduct rigorous academic research whilst evaluating the suitability of methods used and critically examine the limitations of approaches.

Research skills

4. Critically reflect on the AI architecture selected, assessing alternative design approaches and determining how these could enhance system performance or robustness.

Reflection

Visual Studio
VLE
Office 365
Staffordshire University Library
Internet Access
Digital Academy Forum
Digital Academy Upload
Game Lab

Rabin, S. (2008) AI Game Programming Wisdom series, CRC Press.

Rabin, S. (2017) Game AI Pro series, CRC Press.

Roberts, P. (2026) Game AI Uncovered series, CRC Press.

There are a wide range of problems to solve in video games from an AI perspective. Be that navigating a car around a track, having groups of agents move together in formation, squad behaviour, populating a busy marketplace, how to target the player in a way that feels fair, and much more. In this module, you will delve into these areas and look at the potential solutions. You will select a real-world problem to solve and develop your own bespoke solution.