This module will enable students to gain a deeper understanding of agent decision-making within the field of game AI. It focuses both on the practical side of the problem, whilst also requiring academic research to go beyond the module content. The curriculum will look at decision-making in commercially available games, with a focus on:

Finite State Machines
Decision Trees
Behaviour Trees
Reactive Behaviour Trees
Markov systems
Fuzzy Logic
MARKO Methodology
Goal-Oriented Action Planning
Hierarchical Task Networks

Assessment Component 1 – Decision-Making Solution 50% [Learning outcomes: 1, 2]

This assessment requires students to develop a practical solution that demonstrates their understanding of agent decision-making within a game context. A problem will be provided for which the student must conduct their own research to find a suitable approach to solve the problem.

Assessment Component 2 – Literature Review 50% [Learning outcomes: 1, 3 and 4]

This component requires students to produce a written literature review that reviews a given problem within the field of decision-making in game AI development and proposes solutions to set problems that reach beyond the module content.

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 decision-making 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 research whilst evaluating the suitability of methods used and critically examine the limitations of approaches.

Research skills

4. Communicate effectively to professional and non-specialist audiences about the practical implications of the chosen approach(es).

Communication

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

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

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

Seeman, G. (2004) AI for Game Developers, O'Reilly Media.

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

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

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

Game characters make decisions all the time in video games, but if they make the wrong decision, they look unintelligent. There are a variety of different approaches used in games for agent decision-making that include reactive decision-making all the way up to long-term planning. This module will delve into the world of agent-based decision-making and equip you with the skills required to ensure your agents make decisions intelligently.