ELE691 - KNOWLEDGE-BASED SYSTEMS
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| KNOWLEDGE-BASED SYSTEMS | ELE691 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | None | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Problem Solving | |||||
| Instructor (s) | Department Faculty | |||||
| 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 |
| |||||
| 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 |
|---|---|
| Week 1 | Introduction to Knowledge-Based Systems |
| Week 2 | Review of Knowledge-Based Systems as an Artificial Intelligence application |
| Week 3 | Propositional logic , methods of inference and reasoning in propositional logic |
| Week 4 | Predicate logic, methods of inference and reasoning in predicate logic |
| Week 5 | Knowledge representation in propositional and predicate logic, logical reasoning with knowledge base, resolution-refutation |
| Week 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 |
| Week 7 | Semantic networks, reasoning with semantic nets, semantic network operation, frames, frame organization, object-based systems |
| Week 8 | Midterm Exam |
| Week 9 | Search structures: uninformed search, heuristic search, adversarial search, minimax algorithm, alpha-beta pruning |
| Week 10 | Representing uncertainty: Bayesian networks, Bayesian reasoning, temporal reasoning and Markov chains, measures of belief and disbelief, certainty factors, Dempster-Shafer theory, belief functions |
| Week 11 | Approaches to approximate reasoning, fuzzy logic, fuzzy relations, fuzzy reasoning |
| Week 12 | Hybrid intelligent systems: Fuzzy expert systems, neural expert systems, neuro-fuzzy systems. Knowledge acquisition: Sources, levels, and categories of knowledge |
| Week 13 | Alternative approaches in reasoning: Model-based reasoning, case-based reasoning, decision tree algorithm |
| Week 14 | KBS development tools and KBS applications |
| Week 15 | Preparation week for final exams |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 1 | 20 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 1 | 30 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 0 | 50 |
| Percentage of final exam contributing grade succes | 0 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 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, ect) | 14 | 6 | 84 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 8 | 7 | 56 |
| 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
| D.9. Key Learning Outcomes | Contrubition 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. | 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 effectively | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest