ModulespecCOM340new.doc

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ModulespecCOM340new.doc

  1. 1. Module Description Template This description is drawn up in a standard format. It is designed to describe the level of the module, what the student learns to do by undertaking it and how that performance is assessed. The template is laid out in boxed format, which you may choose to follow if you wish. Please type within the cells of the table to facilitate the maintenance of the standard format which you may choose to follow. Add or delete rows if necessary but it should not be necessary to add columns. Please fill in and then delete all italic text [guidance notes] before you print it. In completing this module description, arial font and font size 12 is recommended for purposes of accessibility. MODULE TITLE: Introduction to Knowledge Based Systems MODULE CODE: COM340J2 DATE OF REVISION: 2006/2007 MODULE LEVEL: 2 CREDIT POINTS: 20 MODULE STATUS: Optional] SEMESTER: 2 LOCATION: Jordanstown. E-LEARNING: web supplemented PREREQUISITE(S): None CO-REQUISITE(S): None. MODULE CO-ORDINATOR(S): Patterson, WRD TEACHING STAFF RESPONSIBLE Patterson, WRD FOR MODULE DELIVERY: HOURS: Indicate total notional student effort hours and their division between lectures, seminars, tutorials, practicals, private study, etc (10 hours = 1 credit point) Lectures 24 Seminars 0 Tutorials 12 Practicals 24 Independent study 140 (including assessment) TOTAL EFFORT HOURS: 200 ACADEMIC SUBJECT: Computing MODULAR SUBJECT:
  2. 2. RATIONALE Knowledge-based systems are widely used in business and industry, where they have provided solutions to many complex problems and brought about significant productivity gains. Successful application of the technology requires an understanding of:- the underlying principles of knowledge representation; the different reasoning paradigms available and the ability to ascertain which is most suitable to a particular problem; the skills in a language suitable for KBS implementation. This module provides an introduction to the principles and practice of knowledge-based systems, with appropriate emphasis on programming and implementation issues. AIMS To develop an appreciation of the types of problem-solving applications for which knowledge- based systems are useful. To develop an understanding of the principles of knowledge representation and reasoning. To develop an appreciation for how the field of knowledge based systems has evolved and matured to meet new challenges To develop an understanding of the knowledge acquisition process To develop skills in the use of a programming language suitable for the implementation of knowledge-based systems. To develop knowledge engineering skills. LEARNING OUTCOMES Learning Outcomes should be statements of the minimum that a student will be able to do when they have completed the module. Learning outcomes should: • be written in the future tense; • identify important learning requirements; • be achievable and assessable; and • use language that students can understand. Further advice is available from the University of Ulster Assessment Handbook. Adapt the following table to suit your needs. The categories are those used in the Programme Specification. Not all categories need be addressed in each module and the number of learning outcomes is not fixed. Learning outcomes should be compatible with the level descriptors. See Appendix 8 of the Programme Approval, Management and Review handbook for further information. Follow a numbering scheme since these will be referred to in the Assessment section and will assist in drawing up a programme specification. You may wish to identify learning outcomes that are not essential (i.e. above the minimum) but which nevertheless add value. These need not be assessed. Do not number them. A successful student will be able to show that he/she can:
  3. 3. KNOWLEDGE AND UNDERSTANDING K1 Demonstrate an understanding of different knowledge representation techniques such as semantic networks, decision trees, logic, frames and object orientated approaches, case-based reasoning and hybrid systems. K2 Construct a KBS using an appropriate tool. K3 K4 INTELLECTUAL QUALITIES I1 Specify and design a KBS that employs inferencing I2 I3 I4 PROFESSIONAL/PRACTICAL SKILLS P1 Work as an integral member of a team to develop a KBS P2 P3 P4 TRANSFERABLE SKILLS T1 Communicate key concepts of their work to their peers T2 T3 T4
  4. 4. CONTENT Introduction Introduction to artificial intelligence and knowledge-based systems. Problems solving as search. Role of expertise in problem solving Knowledge-based systems and their applications Historical development and distinctive features of knowledge-based systems. Knowledge-based system architectures. User interface design issues: explanation of reasoning, mixed- initiative dialogue, sensitivity analysis. Tools for building knowledge-based systems. Applications in business, industry and medicine. Advances in knowledge-based systems technology. Knowledge-based systems and their applications Historical development and distinctive features of knowledge-based systems. Knowledge-based system architectures. User interface design issues: explanation of reasoning, mixed- initiative dialogue, sensitivity analysis. Tools for building knowledge-based systems. Applications in business, industry and medicine. Advances in knowledge-based systems technology. Knowledge representation and reasoning strategies Propositional logic and predicate calculus. Reasoning with semantic networks. Frames and object-oriented representations. Production rules: forward chaining, backward chaining, mixed inference strategies, conflict resolution. Decision tree representation of knowledge. Meta-knowledge and explicit control of reasoning. Reasoning in the presence of uncertainty. Case-based reasoning as a problem solving methodology, its processes, knowledge containers and applications Deductive reasoning and model-based reasoning. Knowledge-based systems development Assessing the appropriateness of knowledge-based systems development. Constructive vs classification problem-solving; generic problem-solving methods. Knowledge acquisition: the knowledge engineering bottleneck, source of knowledge, knowledge elicitation techniques process, machine learning and its role in knowledge acquisition. Knowledge base validation and maintenance.
  5. 5. TEACHING AND LEARNING METHODS Lectures will provide students with the history and current practice on the current topics of Knowledge Based Systems. Tutorials will offer the student the opportunities to complete example examination questions and to query the lecturers on any problem areas. Practical exercises will introduce students to “hands on” learning designed to reinforce the theory of the previous weeks lectures. It will include experience of the design and development, of a Hybrid KBS combining both rules, and objects. Students will be directed to a reading at appropriate points in the course The module is web supplemented
  6. 6. ASSESSMENT Course work 1: 20% of overall coursework mark The first assignment is designed to ensure that students understand the processes of forward and backward chaining essential to the operation of any rule based KBS. They are provided with a set of rules and a number of known facts. They have to demonstrate how a conclusion is reached from the facts using a) forward chaining and b) backward chaining. This assignment will measure the student's achievement of learning outcome (iii) for the module COM340J2 Course work 2: 80% of overall coursework mark The second assessment is designed to ensure that the students can design and develop a prototype KBS using rules, frames and objects. They must choose a problem domain and elicit relevant knowledge from an identified source of expertise. They must then build a system which can reason within the domain. They work in groups of 5 for this assignment and are required to present their system as part of an oral presentation to the class. They are assessed based on their presentation and a written report submitted to the lecturer. This assignment will measure the student's achievement of learning outcomes (iv) for the module COM340J2 Examination: The examination is three hours in length consisting of six questions and is closed book. The students are required to answer four questions. It is a closed book exam. The examination will measure the student's achievement of learning outcomes (i), (ii), (iii), (iv), (v) and (vi) for the module COM340J2 Give the distribution of marks between coursework and examination 25 % Coursework 75 % Examination READING LIST List all required and indicative recommended reading. These should include electronic sources. Use the Harvard referencing system throughout: Author, (Year), Title, Place of Publication, Publisher Required Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition) by Michael Negnevitsky. Addison Wesley. 2004 Recommended Expert Systems Design and Development J. Durkin (006.3/DUR)
  7. 7. Artificial Intelligence, Structures and Strategies for Complex Problem Solving. G. Luger & W. Stubblefield (006.3/LUG) SUMMARY DESCRIPTION This module provides an introduction to knowledge-based systems and their applications in business, industry and medicine. Topics covered include the principles of knowledge representation and reasoning, Knowledge-based system architectures, explanation of reasoning, management of uncertainty, roles of meta-knowledge, generic problem-solving methods and knowledge elicitation. Appropriate emphasis is placed on programming and implementation issues. Academic Office January 2006

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