Module Information
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Analytic report on scientific paper(s) (3000 words limit), including presentation and discussion | 40% |
| Semester Assessment | Essay topic in Representation and Reasoning for Intelligent Systems (3000 words) | 60% |
| Supplementary Assessment | Will take a form as agreed by Departmental exam board |
Learning Outcomes
On successful completion of this module students should be able to:
A good understanding of how intelligent systems can represent, reason and react effectively about the environment in which they exist.
Appreciation of the diversity of the existing techniques for knowledge representation and inference as well as their respective strengths and limitations.
Awareness of the current state-of-the-art in both symbolic and semi-symbolic approaches for reasoning and revision with formally represented domain knowledge.
The ability and interest in applying advanced representation and reasoning techniques in solving real-world problems.
The ability to search for, and critically evaluate literature relevant to their assignment topic, as demonstrated in their assignment report
Brief description
Content
Ideas of constraint satisfaction problems and typical algorithms for constraint satisfaction, constraint propagation and other solution techniques.
2. Uncertainty handling techniques: (6 hrs)
Example theories and their utility of representing and reasoning with uncertain knowledge, including Bayesian nets, Dempster-Shafer theory, fuzzy logic and rough set theory.
3. Symbolic belief revision techniques: (4 hrs)
Techniques for an intelligent system to make hypotheses and explore their consequences, covering reason maintenance and assumption-based truth maintenance.
4. Qualitative reasoning: (4 hrs)
Basic approaches for qualitatively representing and reasoning about the structure and behaviour of domain systems, focussing on the constraint-centred modelling paradigm.
5. Model based reasoning: (4 hrs)
Methods and tools for developing systems that utilise explicit models of domain problems, analysing the general diagnostic engine and its extensions, and systems for failure mode and effects analysis.
6. Case based reasoning: (4 hrs)
Principles and basic techniques for exploiting knowledge of experienced cases, discussing important issues of case indexing, case retrieval and case adaptation in such systems.
7. Example applications (4 hrs) of the different techniques/approaches introduced.
Notes
This module is at CQFW Level 7