Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Artificial Intelligence | |||
| Reference | CM2602 | Version | 2 |
| Created | February 2024 | SCQF Level | SCQF 8 |
| Approved | July 2020 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To introduce key concepts of artificial intelligence (AI) (such as search, reasoning and knowledge representation, planning, and learning) needed to develop applications of intelligent systems. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Practice intelligent problem-solving methods and their applications underpinned by AI's philosophical and cognitive theory. |
| 2 | Compare reasoning and knowledge representation strategies used in Artificial Intelligence |
| 3 | Practice suitable search techniques related to AI problem solving. |
| 4 | Distinguish legal, ethical, and professional issues in the context of intelligent systems solutions for real-world applications. |
| Indicative Module Content |
|---|
| Introduction to AI, its history, philosophy and cognitive influence. Agents: rational agency, logical agents, agent architectures and cooperation. Search: uninformed and informed search, constraint satisfaction problems, games. Planning: partial order planning, conditional planning, monitoring and re-planning. Knowledge Representation: logic, rules, frames, semantic networks (ontologies), description logic (Propositional logic, First order predicate logic, Fuzzy logic). |
| Module Delivery |
|---|
| Lectures are used to deliver the main principles underlying problem solving methods. Lab sessions are used to examine case studies which reinforce the material covered in lectures and to design and implement prototype game-playing systems. Knowledge and understanding is further enhanced through directed reading. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 48 | N/A |
| Non-Contact Hours | 102 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Individual coursework covering all learning outcomes. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass this module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1. |
| B | The student needs to achieve a B in C1. |
| C | The student needs to achieve a C in C1. |
| D | The student needs to achieve a D in C1. |
| E | The student needs to achieve an E in C1. |
| F | The student needs to achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | CM1602 or equivalent. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Russell, S. and Norvig, P. 2010. Artificial Intelligence: A Modern Approach. 3rd ed. Prentice Hall. |
| 2 | Luger, G.F. 2009. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. 6th ed. Addison Wesley. |
| 3 | Millington, I. 2019. Artificial Intelligence for Games. 3rd edn. CRC Press. |
| 4 | Bourg, D. 2004. AI for Game Developers: Creating Intelligent Behaviour in Games. O'Reilly. |
| 5 | Buckland, M. 2004. Programming Game AI by Example. Wordware Publishing. |