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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) • • • • • • • • • 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 • • 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