Course Details

ELE 691 Knowledge Based Systems
2019-2020 Fall term information

The course is not open this term

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

Course definition tables are extracted from the ECTS Course Catalog web site of Hacettepe University ( in real-time and displayed here. Please check the appropriate page on the original site against any technical problems.


Course Name Code Semester Theory
Credit ECTS
KNOWLEDGE-BASED SYSTEMS ELE691 Any Semester/Year 3 0 3 8
Course languageTurkish
Course typeElective 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
Question and Answer
Problem Solving
Instructor (s)Department Faculty 
Course objectiveThe purpose of this course is to give students an understanding of various aspects of knowledge-based systems (KBS). This course will also facilitate students to engage in KBS related research topics. 
Learning outcomes
  1. A student completing the course successfully will
  2. L.O.1. Understand the principles by which the KBS work.
  3. L.O.2. Have an understanding of different methodologies of KBS and apply these concepts to implement KBS.
  4. L.O.3. Identify and categorize the problems for which a KBS approach would be appropriate.
  5. L.O.4. Be familiar with a range of KBS applications and with some KBS development tools.
Course ContentFoundations of Knowledge-Based Systems, Propositional and predicate logic, Knowledge representation, Methods of inference and reasoning, Rule-based systems, Semantic networks and frames, Object-based systems, Search structures, Representing uncertainty, Reasoning under uncertainty, Approximate reasoning and fuzzy logic, Hybrid systems, Knowledge acquisition, Alternative approaches in reasoning: case-based reasoning, model-based reasoning, KBS development tools, KBS applications. 
References1. Giarratano J.C., and Riley G.D., Expert Systems -- Principles and Programming, 4/e, Thomson/PWS, 2004.
2. Jackson P., Introduction to Expert Systems, 3/e, Addison-Wesley, 1998.
3. Negnevitsky M., Artificial Intelligence: A Guide to Intelligent Systems, 2/e, Addison-Wesley, 2005.
4. Russell S., and Norvig P., Artificial Intelligence: A Modern Approach, 3/e, Prentice Hall, 2010.

Course outline weekly

Week 1Introduction to Knowledge-Based Systems
Week 2Review of Knowledge-Based Systems as an Artificial Intelligence application
Week 3Propositional logic , methods of inference and reasoning in propositional logic
Week 4Predicate logic, methods of inference and reasoning in predicate logic
Week 5Knowledge representation in propositional and predicate logic, logical reasoning with knowledge base, resolution-refutation
Week 6Rule-based systems: Types of knowledge, knowledge hierarchy, expert system architecture, reasoning with production rules, forward and backward chaining, meta rules, AND-OR graph, conflict resolution strategies
Week 7Semantic networks, reasoning with semantic nets, semantic network operation, frames, frame organization, object-based systems
Week 8Midterm Exam
Week 9Search structures: uninformed search, heuristic search, adversarial search, minimax algorithm, alpha-beta pruning
Week 10Representing uncertainty: Bayesian networks, Bayesian reasoning, temporal reasoning and Markov chains, measures of belief and disbelief, certainty factors, Dempster-Shafer theory, belief functions
Week 11Approaches to approximate reasoning, fuzzy logic, fuzzy relations, fuzzy reasoning
Week 12Hybrid intelligent systems: Fuzzy expert systems, neural expert systems, neuro-fuzzy systems. Knowledge acquisition: Sources, levels, and categories of knowledge
Week 13Alternative approaches in reasoning: Model-based reasoning, case-based reasoning, decision tree algorithm
Week 14KBS development tools and KBS applications
Week 15Preparation week for final exams
Week 16Final exam

Assesment methods

Course activitiesNumberPercentage
Field activities00
Specific practical training00
Final exam150
Percentage of semester activities contributing grade succes050
Percentage of final exam contributing grade succes050

Workload and ECTS calculation

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 14 3 42
Laboratory 0 0 0
Specific practical training000
Field activities000
Study Hours Out of Class (Preliminary work, reinforcement, ect)14684
Presentation / Seminar Preparation000
Homework assignment8756
Midterms (Study duration)12525
Final Exam (Study duration) 13333
Total Workload3874240

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.   X 
2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering.  X  
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.  X  
4. Designs and runs research projects, analyzes and interprets the results. X   
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects. X   
6. Produces novel solutions for problems.  X  
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.  X  
8. Follows technological developments, improves him/herself , easily adapts to new conditions. X    
9. Is aware of ethical, social and environmental impacts of his/her work. X   
10. Can present his/her ideas and works in written and oral form effectively; uses English effectivelyX    

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

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