SHE Level 4
SCQF Credit Points 20.00
ECTS Credit Points 10.00
Module Code MHI625658
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, statistics, data mining, human computer interaction to multi-agent systems, robotics and video games. This module introduces topics from the latest core technologies in AI, with a focus on machine learning. Students will utilise this in their own learning by researching, developing and testing AI techniques in testbed environments to perform critical analysis of their own AI techniques. Much of this module content will be tailored to students own interests in AI in order to deliver personalised learning


-360b7 Decision making: Decision trees; Behaviour trees; Intelligent planning. -360b7 Advanced search techniques: Adversarial search (Minimax, or alpha-beta); Monte Carlo search techniques. -360b7 Machine learning: Artificial Neural networks (ANNs); Genetic Algorithms (GAs and GP); reinforcement learning; Classifier systems. b7 Cognitive architectures: Rule based systems; Emotion models; Multi-agent systems (MAS); Agent based modelling (ABM); Artificial life; Philosophy of AI. -360b7 Alternative topics (research interests, and depending on student interests): Swarm intelligence (PSO / ACO); AI Directors; Natural language processing (NLP); Data mining and analytics; Computational Creativity; Bayesianism; Q-learning and TD-learning. -360

Learning Outcomes

On completion of this module, students should be able to:-1. Critique and assess AI techniques that occur in software technologies against various feasibility and performance criteria.2. Analyse data with traditional statistical methods and with machine learning techniques.3. Propose AI solutions to industrially relevant applications.4. Conduct a small-scale AI project, to evaluate an existing or novel technique.5. Develop algorithms for various AI techniques into future software applications.

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 out with scheduled class time.

Indicative Reading

-360b7 Alpaydin, E. (2014). Introduction to Machine Learning (Adaptive Computation and Machine Learning Series). MIT Press. ISBN-13: 978-0262028189. -360b7 Chollet, F. (2017). Deep Learning with Python. Pub: Manning. ISBN: 9781617294433. b7 Engelbracht, A. (2007). Computational Intelligence. An introduction. 2nd Edition. Wiley. ISBN-13: 978-0-470035610. b7 Michael Wooldridge (2009). An Introduction to MultiAgent Systems. 2nd Edition. Wiley. ISBN-13: 978-0470519462. b7 Norvig, P. and Russell, S. (2016). Artificial Intelligence: A Modern Approach, Global Edition. 3rd Edition. Pearson. ISBN-13: 978-1292153964. b7 Russell and Norvig (2010). Artificial Intelligence: a Modern Approach . 3rd edition. Pearson. ISBN-13: 978-0132071482. -360b7 Yannakakis, G, N. and Togelius, J. (2018). Artificial Intelligence and Games. Springer. ISBN-13: 978-3319635187. -360

Transferrable Skills

D1 Specialist knowledge and application D2 Critical thinking and problem solving D3 Critical analysis D6 Effective information retrieval and research skills D8 Self confidence, self discipline & self reliance (independent working) D9 Awareness of strengths and weaknesses D10 Creativity, innovation & independent thinking D12 Appreciating and desiring the need for continuing professional development D16 Interpersonal skills, team working and leadership

Module Structure

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

Assessment Methods

Component Duration Weighting Threshold Description
Coursework 1 n/a 100.00 40% Practical Based Assignment