Download Course unit Descriptor

Course unit
Descriptor
GENERAL INFORMATION
Study program in which the course unit is
offered
Business Information Systems
Course unit title
Intelligent Systems
Course unit code
OS- 306
Type of course unit 1
Compulsory
Level of course unit 2
Bachelor
Semester when the course unit is offered
Fifth (Winter)
Year of study (if applicable)
Third
Number of ECTS allocated
6 ECTS
Name of lecturer/lecturers
Bošnjak Zita, PhD
Grljević Olivera, PhD
Mode of course unit delivery 3
Face-to-face
Course unit pre-requisites (if any)
Elementary computer literacy
PURPOSE AND OVERVIEW (max 5-10 sentences)
The curriculum gives students an introduction to the idea of intelligent/expert systems and enables them
to explore topics within the field of AI in greater depth. It comprises of topics related to the
development, implementation and business application of expert systems and fuzzy-logic based expert
systems. The novel research results in covered fields are investigated from the theoretical and practical
viewpoint and they provide students necessary knowledge and skills to autonomously conduct
knowledge acquisition, knowledge engineering and develop expert systems and fuzzy systems for
heuristic approach to decision making.
LEARNING OUTCOMES (knowledge and skills)
Students will acquire knowledge for adequate problem selection for the development of expert systems,
master the knowledge engineering skills, be able to apply the knowledge representation paradigms and
create consistent, well-designed knowledge bases as a crucial part of expert systems. They will also
acquire knowledge to understand inherent inference mechanisms and conduct successful ES validation
and testing. Students will be able to select the most appropriate uncertainty management approach for
expert systems, including linguistic uncertainty, and develop an adequate expert system or fuzzy
1
Compulsory, optional
First, second or third cycle (Bachelor, Master's, Doctoral)
3
Face-to-face, distance learning, etc.
2
controller for heuristic decision making in selected software development tool: Corvid expert system
shell.
SYLLABUS (outline and summary of topics)
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Introduction to artificial intelligence and expert systems - historical overview
Heuristic vs. analytic decision making - when and why are ES applicable. Benefits and drawbacks.
Expert system architecture. Problem selection by AHP method.
Knowledge acquisition process. Most common knowledge acquisition techniques (interview,
repertory grids, multidimensional scaling, decision tables and decision trees)
Knowledge representation and structuring for a knowledge base.
Inference mechanisms in rule based expert systems (forward and backward chaining). and
Inference in frame based expert systems (methods and demons).
Uncertainty in expert systems. Conditional probability and confidence factors. Inference under
uncertainty.
Linguistic uncertainty. Fuzzy sets theory. Fuzzy inference. Defuzzyfication methods.
ES development methodology. Protopyping. Implementation steps in Exsys Professional, Corvid,
FuzzyTECH and DataEngine software tools.
LEARNING AND TEACHING (planned learning activities and teaching methods)
Excatedra lectures supported by Power Point presentations, Web-based expert systems case studies and
group disscussions, teamwork in computer lab on knowledge acquisition and development of small scale
expert systems.
REQUIRED READING
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Russell, Stuart J., and Norvig, Peter (2005) Artificial Intelligence: A Modern Approach, Prentice Hall
Series in Artificial Intelligence. Englewood Cliffs, New Jersey
Michael Negnevitsky (2001) Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley.
Pearson Education, Great Britain, chapters 1-5 (till pp. 163)
ASSESSMENT METHODS AND CRITERIA
Students have the opportunity to earn 100 points as follows:
• General Class Participation 6 points (students receive points based on how well they are
actively engaged in a learning process - by asking questions, discussing problem topics,
proposing solutions).
• Two theorethical tests (2x20) 40 points
• Individual* project (lab work) 24 points
• Oral exam 30 points
Final grades will be assigned as follows:
• 95 or above for a 10,
• 85-94% for a 9,
• 75-84% for an 8,
• 65-74% for a 7
• 55-64% for a 6, and
• below 55% for a 5.
*
Within an individual project, any substantive contribution by another person or taken from a publication
or code source should be properly acknowledged in writing. Failure to do so is plagiarism and will
necessitate exam failure and disciplinary actions.
LANGUAGE OF INSTRUCTION
English, Serbian