Assessment Component 1 – Game Agent Movement Artefact 50% [Learning outcomes: 1, 2]

* This component requires students to develop a practical solution that demonstrates their understanding of agent movement within a game context. Students will develop the AI for two different agents throughout the module that will compete to achieve a goal.

Assessment Component 2 – Concept Challenges 50% [Learning outcomes: 1, 3, 4]

* This component requires students to write solutions to a selection of computational problems solved using both algorithmic and programming techniques. The solutions will come in a variety of forms, ranging from pseudo code, Spreadsheets and programmed elements. Further, student answers will demonstrate the efficacy of the techniques used. For each of these answers, students will also be expected to analyse the techniques in terms of formal efficiency notation.

* Students will complete 3 tasks associated with computational problems. These tasks will include:

Brute Force Algorithms
Sorting Algorithms
Divide and Conquer Algorithms

This module introduces the mathematical concepts that underpin the movement of game agents as they navigate through a game world. This includes:

* Asymptotic Notation
* Pathfinding
* Graph Theory
* Data Structures
* Steering Behaviours
* Obstacle Avoidance
* Brute Force Algorithms
* Divide and Conquer Algorithms
* Sorting Algorithms

1. Demonstrate knowledge in the development of solutions to computational problems within the realm of artificial intelligence, based on algorithmic methods.

Programme Learning Outcome: Knowledge & understanding

2. Evaluate the appropriateness of different approaches to construct a working solution to an artificial intelligence problem.

Programme Learning Outcome: Application & problem-solving

3. Use appropriate research techniques to locate, examine and justify the mathematical principles that underpin computational problem-solving used within AI

Programme Learning Outcome: Research skills

4. Communicate effectively to convey a clear rational for approaches taken to develop an artificial intelligence solution.

Programme Learning Outcome: Communication

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.

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

Cormen, T. H., Leiserson, C. E., Rivest, R. l., and Stein, C. (2009) Introduction to Algorithms. 3rd Edition. Massachusetts: MIT Press.

Buckland, M. (2004) Game AI Programming by Example, Wordware Press.

Millington, I. (2019) AI for Games. 3rd Edition. CRC Press.

Roberts, P. (2023) Artificial Intelligence in Games, CRC Press.

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

Roughgarden, T. (2022) Algorithms Illuminated. Cambridge: Cambridge university Press

Computer games are populated with characters that flesh out the world. These characters need to navigate their way through the environment in an intelligent manner, whilst avoiding collisions with the player and other characters. This module will introduce you to the mathematical concepts that underpin this behaviour.