ACADEMICS
Course Details

ELE691 - Knowledge Based Systems

2022-2023 Fall term information
The course is not open this term
ELE691 - Knowledge Based Systems
Program Theoretýcal hours Practical hours Local credit ECTS credit
MS 3 0 3 8
Obligation : Elective
Prerequisite courses : -
Concurrent courses : -
Delivery modes : Face-to-Face
Learning and teaching strategies : Lecture, Question and Answer, Problem Solving
Course objective : The 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 : A student completing the course successfully will Understand the principles by which the KBS work. Have an understanding of different methodologies of KBS and apply these concepts to implement KBS. Identify and categorize the problems for which a KBS approach would be appropriate. Be familiar with a range of KBS applications and with some KBS development tools.
Course content : Foundations 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.
References : 1. 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
Weeks Topics
1 Introduction to Knowledge-Based Systems
2 Review of Knowledge-Based Systems as an Artificial Intelligence application
3 Propositional logic , methods of inference and reasoning in propositional logic
4 Predicate logic, methods of inference and reasoning in predicate logic
5 Knowledge representation in propositional and predicate logic, logical reasoning with knowledge base, resolution-refutation
6 Rule-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
7 Semantic networks, reasoning with semantic nets, semantic network operation, frames, frame organization, object-based systems
8 Midterm Exam
9 Search structures: uninformed search, heuristic search, adversarial search, minimax algorithm, alpha-beta pruning
10 Representing uncertainty: Bayesian networks, Bayesian reasoning, temporal reasoning and Markov chains, measures of belief and disbelief, certainty factors, Dempster-Shafer theory, belief functions
11 Approaches to approximate reasoning, fuzzy logic, fuzzy relations, fuzzy reasoning
12 Hybrid intelligent systems: Fuzzy expert systems, neural expert systems, neuro-fuzzy systems. Knowledge acquisition: Sources, levels, and categories of knowledge
13 Alternative approaches in reasoning: Model-based reasoning, case-based reasoning, decision tree algorithm
14 KBS development tools and KBS applications
15 Preparation week for final exams
16 Final exam
Assessment Methods
Course activities Number Percentage
Attendance 0 0
Laboratory 0 0
Application 0 0
Field activities 0 0
Specific practical training 0 0
Assignments 8 20
Presentation 0 0
Project 0 0
Seminar 0 0
Quiz 0 0
Midterms 1 30
Final exam 1 50
Total 100
Percentage of semester activities contributing grade success 50
Percentage of final exam contributing grade success 50
Total 100
Workload and ECTS Calculation
Course activities Number Duration (hours) Total workload
Course Duration 14 3 42
Laboratory 0 0 0
Application 0 0 0
Specific practical training 0 0 0
Field activities 0 0 0
Study Hours Out of Class (Preliminary work, reinforcement, etc.) 14 6 84
Presentation / Seminar Preparation 0 0 0
Project 0 0 0
Homework assignment 8 7 56
Quiz 0 0 0
Midterms (Study duration) 1 25 25
Final Exam (Study duration) 1 33 33
Total workload 38 74 240
Matrix Of The Course Learning Outcomes Versus Program Outcomes
Key learning outcomes Contribution level
1 2 3 4 5
1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge.
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.
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.
4. Designs and runs research projects, analyzes and interprets the results.
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.
6. Produces novel solutions for problems.
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.
8. Follows technological developments, improves him/herself , easily adapts to new conditions.
9. Is aware of ethical, social and environmental impacts of his/her work.
10. Can present his/her ideas and works in written and oral form effectively; uses English effectively.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
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