CE811-7-SU-CO:
Game Artificial Intelligence
PLEASE NOTE: This module is inactive. Visit the Module Directory to view modules and variants offered during the current academic year.
2020/21
Computer Science and Electronic Engineering (School of)
Colchester Campus
Summer
Postgraduate: Level 7
Inactive
Monday 26 April 2021
Friday 02 July 2021
15
30 July 2020
Requisites for this module
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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 single agents able to play multiple games. The course also covers Procedural Content Generation, and explores the techniques used to simulate intelligence in the latest videogames.
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 general video game playing and how agents able to play a range of different games at once can be programmed in a high-level language.
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.
* Rolling Horizon Evolutionary Algorithms and Open/Closed Loop Planning.
* Adversarial Games: Minimax, Alpha-Beta Search, Monte Carlo Tree Search.
* A Case Study: General Video Game Playing.
* Steering Behaviours for Autonomous Characters.
* Path-finding: A*, 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.
10 x 2 hour lectures
10 x 2 hour labs
2 x 1 hour progress tests (within the 2 hour lecture slots in weeks 6 and 11)
- Millington, Ian. (2019) AI for games, Boca Raton, FL: CRC Press, Taylor & Francis Group.
- Reynolds, Craig. (1999) Steering Behaviors For Autonomous Characters.
- Sutton, Richard S.; Barto, Andrew G. (2018) Reinforcement Learning: An Introduction, Cambridge, USA: The MIT Press.
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 |
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Additional coursework information
The course will include the use of open-source game rameworks such as GVAI.
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, Dr Katerina Bourazeri
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
No
No
No
Dr Robert Mark Stevenson
University of Sheffield
Senior Lecturer
Available via Moodle
No lecture recording information available for this module.
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