|Delivery Type||Delivery length / details|
|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Development of practical application||50%|
|Supplementary Assessment||Development of practical application||50%|
On successful completion of this module students should be able to:
1. Apply knowledge-based technologies to appropriate real world problems.
2. Identify and select appropriate technologies for specific problems in need of a knowledge-based solution.
3. Determine, justify and use appropriate methodologies for the construction on knowledge-based systems.
The module will consider a range of successful applications of knowledge based systems, identifying the kinds of applications where deployment of each type of knowledge based system is likely to prove effective. It will draw general lessons from the case studies, and consider suitable methodologies for the development of knowledge based systems.
These lectures will introduce what factors to consider in building practical knowledge-based diagnostic systems that automate human expertise. It will look at commercial applications of diagnostic trees, diagnostic test benches and issues generally associated with rule-based systems.
2. Case-based systems - 2 lectures.
Case-based systems exploit records of past experience to enable companies to increase efficiency and reduce cost by automating processes such as scheduling, design paramaterization and diagnosis. These lectures will consider the generic approach to building such systems and their applications in a wide range of areas, supported with demonstrations.
3. Model-based systems - 2 lectures
Key knowledge based applications involve the use of models as a basis for reasoning about the real world. These lectures will examine and explain how model-based reasoning works via hypothesis-making and consequence exploitation,and identify the conditions necessary for successful deployment of this technology.
4. Real-time knowledge-based systems - 2 lectures
These lectures will identify the requirements, principles and success criteria for real-time knowledge-based systems that perform in continuous domains. Application systems for plant monitoring and control will be examined to demonstrate the underlying approaches.
5. Methodologies for knowledge-based systems - 2 lectures
The use of standard development methodologies for knowledge-based systems can be inappropriate because of the difficulty of defining the end product. The appropriateness of agile methodologies for knowledge-based systems will be considered, along with specific KBS methodologies such as common KADS.
6. Agent-based technologies - 4 lectures
An important aspect of the expansion of the Internet is the use of softbot technologies to produce autonomous agents able to either wander the Internet gathering knowledge, or to filter that knowledge in line with the demands of the Internet user. These lectures will explore the composition, deployment and influence in society of such agents.
7. The Semantic Web - 4 lectures
The Semantic Web is the framework in which knowledge stored in the World Wide Web is given meaning. As potentially the largest knowledge base in the world, we look at the tools and technologies used to engineer accessible knowledge in the Web. These lectures cover existing information retrieval methods, Semantic Web infrastucture (XML, RDF, URIs and related Web standards), and commons and commonsense knowledge repositories.
8. Web Services and the Grid - 2 lectures
Software agents interact with the knowledge and information of the Web through Semantic Web structure and through Web Services interfaces. Web Services allow well defined interfaces for agents to access information. The Web Services Resource Framework (WSRF) allows those services to be stateful, and the Grid technology will build on WSRF to allow authorisation, authentication, job management and various other ideas necessary for future web agent based systems. These lectures take a look at these emerging ideas and standards.
|Skills Type||Skills details|
|Application of Number||No|
|Improving own Learning and Performance||The assessed coursework requires students to develop their understanding of issues associated with the module.|
|Information Technology||The module is IT focused. Students will use computer tools to develop and run their applications|
|Personal Development and Career planning||The module gives students a wider view of the computing industry and potential careers|
|Problem solving||In construction of practical application|
|Research skills||Students will be required to acquire further knowledge from journals and on-line sources for essay|
|Subject Specific Skills||Methodological skills, design skills, programming skills|
|Team work||Team work is required during the tutorial and assignment|
Reading ListGeneral Text
Kendal, S. L. (c2007.) An introduction to knowledge engineering /S.L. Kendal and M. Creen. Springer Primo search Negnevitsky, Michael. (c2011.) Artificial intelligence :a guide to intelligent systems /Michael Negnevitsky. 3rd ed. Addison Wesley Primo search Price, Chris. (1999.) Computer-based diagnostic systems. Springer Primo search Russell, Stuart J. (c2010.) Artificial intelligence :a modern approach /Stuart J. Russell and Peter Norvig ; contributing writers, Ernest Davis ... [et al.]. 3rd ed. Prentice Hall Primo search Smith, Peter (c1996.) An introduction to knowledge engineering /Peter Smith. International Thomson Computer Press Primo search Supplementary Text
Magoules, Frederic (2009) Introduction to Grid Computing CRC Press Primo search
Antoniou, G. (c2008.) A semantic Web primer /Grigoris Antoniou and Frank van Harmelen. http://www.loc.gov/catdir/toc/ecip0718/2007020429.html 2nd ed. MIT Press
This module is at CQFW Level 6