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2010/1 Module Catalogue
 Module Code: ENGM075 Module Title: KNOWLEDGE BASED SYSTEMS & ARTIFICIAL INTELLIGENCE
Module Provider: Civil, Chemical & Enviromental Eng Short Name: SE2M45
Level: M Module Co-ordinator: CECELJA F Dr (C, C & E Eng)
Number of credits: 15 Number of ECTS credits: 7.5
 
Module Availability

Spring Semester
Assessment Pattern

Components of Assessment
Method(s)
Weighting
Examination
2-hour unseen examination
60%
Coursework
 Coursework
40%

 
Qualifying Condition(s)
A weighted aggregate mark of 50% is required to pass the module.
Module Overview
Knowledge is the most critical part in any decision making process, being it the process of design, management or general business.  Last several decades have witnessed numerous attempts of presenting knowledge in a form processable by computers, starting with the descriptive logic, through the production rules and ending with the ontologies as the latest stage known today, the process known as artificial intelligence.  This module will help you in consolidating all of these technologies and using them in supporting decision making in the area of your interest, primarily in the process of design for processing industry.
Prerequisites/Co-requisites

None
Module Aims

The aims of the module are to present the current understanding of the development of decision support systems and knowledge management systems. It will be used as a common thread and example of the design of chemical processes, focusing on the use of artificial intelligence techniques such as knowledge representation, knowledge-based decision support and agents technology. Students will be provided with state-of-the-art versions or demonstrations of decision support systems.
Learning Outcomes
On successful completion of the module, you will be able to:
·         Represent a design process as a space of states
·         Demonstrate the competence in understanding the relationship between design artefact, design intent and design rationale
·         Record design rationale in a systematic way and use those records during re-design and retrofit
·         Design a decision support system
·         Identify the representation and management techniques appropriate for a particular problem
·         Build a production rule based decision support system for a specific problem
·         Build an ontology in a specific area of application

Apply an agent-based architecture to the solution of a problem.
Module Content
Process design
·         Conceptual design in Chemical Engineering
·         Modelling the conceptual design process
·         A design process representation
Decision support systems
·         Overview
·         A historical perspective of design support systems in process engineering
·         Knowledge-based decision support
Knowledge representation and management
·         Introduction and scope
·         Production rules as a knowledge representation methodology
·         Development and use of rules in decision making proces
·         Ontologies as a Knowledge Representation formalism
·         Overview of languages to express ontologies
·         Development and use of ontologies
·         Classification and types of agents

Application to decision support
Methods of Teaching/Learning
Lectures, group project, tutorials.
Total student learning time 150 hours.
Selected Texts/Journals
Matthew Horridge, Holger Knublauch, Alan Rector, Robert Stevens, Chris Wroe: A Practical Guide To Building OWL Ontologies Using The Protege-OWL Plugin and CO-ODE Tools Edition 1.0, The University Of Manchester (2007)
Giarratano J, Riley G: Expert systems - principles and programming. Boston, U.S.A.:
PWS Publishing Company, 1999.
 
Required reading
None
Recommended background reading
Turban E and Aronson JE, Decision Support Systems and Intelligent Systems, 6th ed, Prentice Hall, 2001.
Biegler LT, Grossmann IE and Westerberg AW, Systematic Methods of Chemical Process Design, Prentice Hall, 1997.
Nikolopoulos C: Expert Systems, First ed. New York: Marcel Dekker, Inc., 1997.
Negnevitsky M: Arti
cial intelligence, First ed. Harlow, U.K.: Pearson Education Ltd,
2002.
Chen Z: Computational intelligence for decision support. New York: CRC Press,
2000.
Rich E, Knight K: Arti
cial intelligence, International ed. New York: McGraw-Hill, Inc.,
1991.
Noy NF,McGuinness DL: Ontology Development 101: A Guide to Creating Your
First Ontology, Stanford University, Stanford, CA, 94305

World Wide Web Consortium (W3C) web page - http://www.w3.org/2004/OWL/
Last Updated

02/10/2009