CS4S770 - Knowledge-Based Systems 01 Sep 2022 - 31 Aug 2028 | Version 2
Associated Module Information
| Module Code: | CS4S770 | ||
|---|---|---|---|
| Module Title: | Knowledge-Based Systems | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Computer Science | ||
| Module Leader: | Andrew Ware | ||
| Module Team: | Peter Parody, Carl Jones | ||
| First Intended Intake: | SEP 2019 | Final Year of Intake: | |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100963 - knowledge and information systems | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 2 |
|---|---|
| Valid From | 01 Sep 2022 |
| Valid To | 31 Aug 2028 |
Module Aims
To provide a broad introduction to applicable artificial intelligence, the design and development of knowledge-based techniques to support human decision-making, learning and action, and their implications to society.
Content Summary
Introduction to symbolic Artificial Intelligence
Knowledge-based systems (KBS), their development and usage
KBS implementation: design, models and methods, tools, decision support mechanisms, user interactions, organisational issues, knowledge acquisition and representation, system architecture.
Computational and Artificial Intelligence based systems and uncertain information processes. Reasoning, case-based reasoning, logic, fuzzy logic, relations and inference.
Constraint satisfaction, heuristics and decision trees. Natural language processing, semantic web and knowledge automation.
Ethical considerations, implications to society and interpretability.
Big data techniques and methodologies.
Data-driven information systems and knowledge acquisition.
Cognitive interaction and intelligent human interfaces.
Recommender systems and E-service personalisation.
Intelligent decision support systems, prediction systems and warning systems.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 24 |
| Practical classes and workshops | 24 |
| Independent Study | 80 |
| Directed Study | 72 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To design and implement a knowledge-based system utilising appropriate methods, tools and models. |
| LO2 | To critically explain, compare and contrast knowledge-based systems techniques and their use in the support of human decision-making, learning and actions. |
| LO3 | To explain societal and ethical issues associated with Artificial Intelligence. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Practical Written Work 1 | Apply practical knowledge to the solution of a knowledge-based system problem and report on findings in the style of an academic paper. | 0 | 3000 | 50 | No | 40 |
| Asynchronous Assessment | Poster 1 | Apply practical knowledge to the solution of a knowledge-based system problem and report on findings in the style of an academic poster supported by an oral presentation. | 0 | 1500 | 50 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | LO3 | |
| Practical Written Work 1 | ✔ | ✔ | ✔ |
| Poster 1 | ✔ | ✔ | ✔ |