University :Mansoura University |
Faculty :Faculty of Computers and Information |
Department :Computer Science |
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| 1- Course data :- |
| | Code: | عح475 | | Course title: | Expert Systems Development | | Year/Level: | رابعة نظم المعلومات | | Program Title: | | | Specialization: | | | Teaching Hours: | Theoretical: | 3 | Tutorial: | | Practical: | 3 |
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| 2- Course aims :- |
| - This course introduces students to learn how to build expert systems in a variety of application areas.
- Learn the student how knowledge Engineer select the soaftware and hardware tools for the project, extract the necessary knowledge from the Domain Expert and implement the knowledge in a correct and efficient knowledge base.
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| 3- Course Learning Outcomes :- |
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| 4- Course contents :- |
| | No | Topics | Week |
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| 1 | Introduction to : knowledgebased expert systems, conventional programming versus knowledge engineering | | | 2 | Human problem solving : Human knowledge Acquisition - the production system as a processing model - problem solving - varieties of knowledge - the nature of experties. | | | 3 | Knowledge Representation issues. | | | 4 | Strategies for representing knowledge : Using Predicate Logic - Resolution in Predicate Logic. | | | 5 | representing knowledge : Using Rules - Forward and Backward Reasoning. | | | 6 | representing knowledge : Using Semanic network. | | | 7 | representing knowledge : Using Frames. | | | 8 | Uncerainty - Symbolic Reasoning under Uncerainty. | | | 9 | Languages and tools : Levels of soaftware, AI language and environments. | | | 10 | Building Expert Systems in Prolog. | |
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| 5- Teaching and learning methods :- |
| | S | Method |
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| Lectures | | Practical work | | Term project to build a prototype for Real expert system |
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| 6- Teaching and learning methods of disables :- |
| No data found. |
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| 7- Student assessment :- |
| A. Timing |
| | No | Method | Week |
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| 1 | Mid_Term Exam | 8 | | 2 | Practical Exame | 12 | | 3 | Oral EXam and descussion to the Term project | 14 | | 4 | Final Term Exam | 16 |
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| B. Degree |
| | No | Method | Degree |
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| 1 | Mid_term examination | 5 | | 2 | Final_term examination | 75 | | 3 | Oral examination | 5 | | 4 | Practical examination | 10 | | 5 | Semester work | 5 | | 6 | Other types of asessment | | | Total | 100% |
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| 8- List of books and references |
| | S | Item | Type |
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| 1 | An Introduction to Expert Systems ( Knowledge Base ) | | | 2 | DAN W. PATTERSON, " Artificial Intelligence an Expert Systems " 2004 | | | 3 | Peter Jackson, " Introduction to Expert Systems " | | | 4 | Robert I. Levine, " A Comperhensive Guide to AI and Expert Systems " | |
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| 9- Matrix of knowledge and skills of the course |
| | S | Content | Study week |
|---|
| Introduction to : knowledgebased expert systems, conventional programming versus knowledge engineering | | | Human problem solving : Human knowledge Acquisition - the production system as a processing model - problem solving - varieties of knowledge - the nature of experties. | | | Knowledge Representation issues. | | | Strategies for representing knowledge : Using Predicate Logic - Resolution in Predicate Logic. | | | representing knowledge : Using Rules - Forward and Backward Reasoning. | | | representing knowledge : Using Semanic network. | | | representing knowledge : Using Frames. | | | Uncerainty - Symbolic Reasoning under Uncerainty. | | | Languages and tools : Levels of soaftware, AI language and environments. | | | Building Expert Systems in Prolog. | |
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| Course Coordinator(s): - |
| - Tarek Tawfek Ahmed Hamza
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| Head of department: - |
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