CE811-7-AU-CO:
Game Artificial Intelligence
2023/24
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 05 October 2023
Friday 15 December 2023
15
07 June 2023
Requisites for this module
(none)
(none)
(none)
(none)
(none)
MSC G400CH Advanced Computer Science,
MSC G411CH Artificial Intelligence,
MSC G61012 Computer Games
This module covers a range of Artificial Intelligence techniques employed in games, and teaches how games are and can be used for research in Artificial Intelligence.
The module explores algorithms for creating agents that play classical board games (such as chess or checkers) and real-time games (Mario or PacMan), including applying deep learning and reinforcement learning to play games.
The course also covers Procedural Content Generation, and explores the techniques used to simulate intelligence in the latest videogames.
Students should either be strong programmers or have some previous Python programming experience to take this module. The module starts assuming students have proficiency in Python programming from the very first week's lab exercise.
The aim of this course is to provide the student with the core knowledge of the techniques behind the most popular algorithms for Artificial Intelligence. Students will be able to program the agents that play a range of different games, either by themselves or as opponents the player must face. Additionally, the aim of the course is to teach how automatic generation of content (complete games, rules, levels and gameplay elements) can be achieved by means of Artificial Intelligence. The course will also teach the basics of deep learning and reinforcement learning, and how these can be used to create agents which are capable of playing a range of different games.
On successful completion of the course, the students should be able to:
1. Understand the techniques behind algorithms that play real-time video games and adversarial board games.
2. Design and program an agent that is able to play any given real-time or adversarial board game.
3. Implement algorithms that generate content for games procedurally, according to some predefined goals.
4. Implement non-player character behaviours for video games that create the illusion of intelligence.
Syllabus
Basics of Reinforcement Learning: Value Iteration, Policy iteration, Monte Carlo, Temporal Difference Learning, Q-Learning.
The Multi-Armed Bandit Problem, Upper Confidence Bound for Trees, Monte Carlo Tree Search.
Nature Inspired Intelligence: Neural Networks and Genetic Algorithms
Adversarial Games: Minimax, Alpha-Beta Search, Monte Carlo Tree Search.
A Case Study: Deep Learning in Game Playing
Steering Behaviours for Autonomous Characters.
Path-finding: A*, Dijkstra's algorithm, Waypoint-based path-finding, Navigational Meshes.
Agent Behaviour: Finite State Machines, Hierarchical FSMs, Behaviour Trees.
Simulating Intelligence: Sensory Systems, Animations, Goal-Oriented Action Planning.
Procedural Content Generation for Games.
Refresher information on Vectors, Probability and Calculus will be provided during this module.
The course will be taught via lectures and extensive laboratory exercises, using Python as the main programming language. Note that students should either be strong programmers, or have previous Python programming experience, to take this module, as the Python programming will be fairly demanding as soon as the module starts.
8 x 2 hour lectures
10 x 2 hour labs
2 x 1 hour progress tests (in weeks 6 and 11)
Weekly Moodle Quizzes.
Additional video lectures.
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's
reading list.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Progress Test 1 |
|
15% |
| Coursework |
Progress Test 2 |
|
15% |
| Coursework |
Labs and Quizzes - Week 16 |
|
20% |
| Coursework |
Assignment Part 1 |
|
25% |
| Coursework |
Assignment Part 2 |
|
25% |
Additional coursework information
The course will include the use of open-source game frameworks.
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Michael Fairbank, email: m.fairbank@essex.ac.uk.
Dr Michael Fairbank
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Yes
No
Yes
Dr MARJORY CRISTIANY Da COSTA ABREU
Sheffield Hallam University
Senior Lecturer
Available via Moodle
Of 42 hours, 18 (42.9%) hours available to students:
20 hours not recorded due to service coverage or fault;
4 hours not recorded due to opt-out by lecturer(s), module, or event type.
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