CS3S667 - Artificial Intelligence for Game Developers 01 Jul 2022 - 31 Aug 2028 | Version 3
Associated Module Information
| Module Code: | CS3S667 | ||
|---|---|---|---|
| Module Title: | Artificial Intelligence for Game Developers | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Computer Science | ||
| Module Leader: | Mike Reddy | ||
| Module Team: | Christopher Tubb | ||
| First Intended Intake: | SEP 2017 | Final Year of Intake: | |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 6 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 101020 - computer games programming | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 3 |
|---|---|
| Valid From | 01 Jul 2022 |
| Valid To | 31 Aug 2028 |
Module Aims
To appraise the students of the importance of artificial intelligence within today’s game industry
To develop the skills necessary to apply and integrate cutting-edge AI with more established AI techniques in modern games
Content Summary
Introduction to Game AI: controlling entities in games
Movement Algorithms: steering behaviors, path-finding, flocking , grid traversal, search algorithms and cooperative AI
Decision Making: decision trees, state machines, fuzzy logic, goal-oriented behaviour, rule-based systems, scripting, Markov systems
Tactical and Strategic AI: waypoint networks, tactical analyses, coordinated action
Machine Learning: action prediction, reinforcement learning, artificial neural networks and case based reasoning, Genetic Algorithms
AI Engine Integration
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 24 |
| Practical classes and workshops | 24 |
| Independent Study | 80 |
| Directed Study | 72 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Understand the theory that underpins, and the pragmatic difficulties associated with, the development of a working AI game system |
| LO2 | Evaluate the relative effectiveness of different approaches to AI for a given problem |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Practical Coursework 2 (Asynch) | Apply knowledge to the solution of a machine learning problem and report on findings | 0 | 3000 | 50 | No | 40 |
| Asynchronous Assessment | Practical Coursework 1 (Asynch) | Apply knowledge to the solution of a navigation/planning problem and report on findings | 0 | 3000 | 50 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Practical Coursework 2 (Asynch) | ✔ | ✔ | |
| Practical Coursework 1 (Asynch) | ✔ | ✔ | |