SHE Level 4
SCQF Credit Points 20.00
ECTS Credit Points 10.00
Module Code MHI625277
Module Leader David Moffat
School School of Computing, Engineering and Built Environment
Subject Applied Computer Games
  • B (January start)

Summary of Content

Artificial Intelligence (AI) is increasingly important to computer science, with applications from knowledge processing, natural language processing and multi-agent systems to robotics and video games. This module introduces topics from the core technologies in AI, but with a focus on machine learning. Example application areas will include games, human computer interaction and believability of intelligent systems. While AI has wide applicability, games offer an ideal application area, both because they provide a convenient virtual world for artificial agents to inhabit, and because the game industry has great potential benefit yet to gain from AI techniques. Towards the end of the module a selection from the latest developments and teaching staff's own research interests will be introduced, such as cognitive modelling, computational creativity and data mining. The topics in the module will to some extent be fitted to the interests of the students taking it, to personalise learning. It has no formal prerequisites, because any basic techniques (like search, from an earlier module) that it builds upon will be recapped to enable all students to catch up. This makes the module more flexible, and as such it is suitable for students from across computing, as long as they are competent programmers.


-359? Decision making -359? decision trees -359? behaviour trees ? intelligent planning -359? Advanced search techniques (with example applications in games) -359? Adversarial search (Minimax, or alpha-beta) -359? Monte Carlo search techniques -359? Machine learning -359? Artificial Neural networks (ANNs) -359? Genetic Algorithms (GAs; and GP) -359? Cognitive architectures -359? Rule based systems -359? Emotion models -359? Multi-agent systems (MAS) -359? Game theory -359? Agent based modelling (ABM) -359? Alternative topics (research interests, and depending on student interests) -359? Swarm intelligence (PSO / ACO) -359? AI Directors for games ? Natural language processing (NLP) ? Data mining and analytics ? Computational Creativity ? Bayesianism, Q-learning, TD-learning

Learning Outcomes

On completion of this module, students should be able to:-- Appreciate the strengths and weaknesses of different machine learning techniques- Make video games more compelling by inclusion of improved AI- Devise appropriate AI software solutions to novel problems- Analyse the AI techniques that occur in a wide range of software technologies

Teaching / Learning Strategy

The University 'Strategy for Learning' documentation has informed the learning and teaching strategy for this module. The module material will be introduced through lectures, while practical exercises, based on the lecture material, will be given to students for their laboratory sessions. Tutorials will be used to help explain and elaborate on both the lecture material and the laboratory exercises. Full use will be made of GCU Learn to provide Lecture-based and related study materials, along with sample solutions of Tutorial and Laboratory exercises, thus encouraging the development of independent learning and allowing self-reflective feedback on student performance. Staff-based feedback on student performance for submitted work will be provided in line with the University feedback policy, with summative feedback and grades on the coursework assessment utilising GCU Learn. The additional interactive discussion features of GCU Learn will be utilised, as appropriate to the module, to stimulate independent and flexible student learning outwith scheduled class time.

Indicative Reading

Ethem Alpaydin (2004) Introduction to Machine Learning . MIT Press. Andries Engelbracht (2002). Computational Intelligence. An introduction . Wiley. Penny Baillie-de Byl (2004). Programming believable characters for computer games . Charles River Media. Russell and Norvig (3rd edition, 2010). Artificial Intelligence: a Modern Approach . Pearson. Ian Millington and John Funge (2009, 2nd Edition). Artificial Intelligence for Games . Morgan Kaufmann.

Transferrable Skills

-359? critical thinking and analysis -359? research and scholarship ? creativity; divergent thinking ? self-confidence and independent working ? appreciating the need for continuing professional development ? interpersonal skills, team-working, leadership ? communication (written, oral and listening) ? presentation skills ? some awareness of strengths, weaknesses and learning style

Module Structure

Activity Total Hours
Practicals (FT) 24.00
Assessment (FT) 20.00
Tutorials (FT) 12.00
Lectures (FT) 24.00
Independent Learning (FT) 120.00

Assessment Methods

Component Duration Weighting Threshold Description
Coursework 1 n/a 100.00 40% Report (6000 words) of practical work