|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Report: 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||Note - Students must resit failed examination and/or resubmission of failed/non-submitted coursework components or ones of equivalent value.||100%|
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
The module will present a variety of advanced topics relevant to the building of practical intelligent systems. In addition to standard knowledge representation and reasoning techniques, it will cover most recent developments as well. The module will introduce the keep concepts taken by individual approaches and discuss the pros and cons of them. Students will be required to carry out independent review of carefully selected research papers and to present their findings in class seminars.
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.
|Skills Type||Skills details|
|Application of Number||Inherent to subject|
|Improving own Learning and Performance||Inherent to subject|
|Information Technology||Inherent to subject|
|Personal Development and Career planning||Encourages students to see roles in subject for career and personal development|
|Problem solving||Inherent to subject|
|Subject Specific Skills||Representation and Reasoning for Intelligent Systems.|
Reading ListGeneral Text
(1984.) Qualitative reasoning about physical systems /edited by Daniel G. Bobrow. North-Holland Primo search Kuipers, Benjamin. (1994.) Qualitative reasoning :modeling and simulation with incomplete knowledge /Benjamin Kuipers. MIT Press Primo search Negnevitsky, Michael. (2011.) Artificial intelligencea guide to intelligent systems / Michael Negnevitsky. https://www.dawsonera.com/guard/protected/dawson.jsp?name=https://shibboleth.aber.ac.uk/shibboleth&amp;dest=http://www.dawsonera.com/depp/reader/protected/external/AbstractView/S9781408225752 3rd ed. Addison Wesley Parsons, Simon. (2001.) Qualitative methods for reasoning under uncertainty /Simon Parsons. MIT Press Primo search Essential Reading
David Barber (2012) Bayesian Reasoning and Machine Learning http://www.cs.ucl.ac.uk/staff/d.barber/brml/ available free online Cambridge University Press Russell, Stuart J. (2010.) Artificial intelligence :a modern approach /Stuart J. Russell and Peter Norvig ; contributing writers, Ernest Davis ... [et al.]. 3rd ed. Prentice Hall ; Primo search Shen, Qiang (2000) Practical Reasoning Methodologies Lecture notes. University of Edingburgh Primo search
This module is at CQFW Level 7