Artificial Intelligence for Games
Duration
- One Semester or equivalent
Contact hours
- 24 hours face to face + blended
On-campus unit delivery combines face-to-face and digital learning.
Prerequisites
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Aims and objectives
The aim of this unit is for students to understand and utilise artificial intelligence concepts and techniques for game environments and game development.
Unit Learning Outcomes (ULO)
Students who successfully complete this unit will be able to:
1. Discuss and implement software development techniques to support the creation of AI behaviour in games
2. Understand and utilise a variety of graph and path planning techniques
3. Create realistic movement for agents using steering force models
4. Create agents that are capable of planning actions in order to achieve goals
5. Combine AI techniques to create more advanced game AI
Unit Learning Outcomes (ULO)
Students who successfully complete this unit will be able to:
1. Discuss and implement software development techniques to support the creation of AI behaviour in games
2. Understand and utilise a variety of graph and path planning techniques
3. Create realistic movement for agents using steering force models
4. Create agents that are capable of planning actions in order to achieve goals
5. Combine AI techniques to create more advanced game AI
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Hawthorn
Type | Hours per week | Number of Weeks | Total |
Live Online Lecture | 1 | 12 | 12 |
| On Campus Class (computer lab) | 2 | 12 | 24 |
| Online Activities | 1 | 12 | 12 |
Unspecified Activities Independent Learning | 8.5 | 12 | 102 |
TOTAL | 150 hours |
Assessment
| Types | Individual or Group task | Weighting | Assesses attainment of these ULOs | ||
| Portfolio | Individual | 100% | 1,2,3,4,5 | ||
Content
• Search and optimisation
• Knowledge representation, reasoning systems, machine learning
• Evolutionary systems, artificial neural networks, collective systems
• Game theory, design and development, rule design, game balancing
• Scripting methods
• AI evaluation
• Knowledge representation, reasoning systems, machine learning
• Evolutionary systems, artificial neural networks, collective systems
• Game theory, design and development, rule design, game balancing
• Scripting methods
• AI evaluation
Study resources
- Reading materials.
Reading materials
A list of reading materials and/or required texts will be made available in the Unit Outline