Introduction to Expert Systems (MAE846)

General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Undergraduate
Course Code	MAE846
Semester	8
Course Title	Introduction to Expert Systems
Independent Teaching Activities	Lectures (Weekly Teaching Hours: 3, Credits: 6)
Course Type	Special Background
Prerequisite Courses	Logic Programming, Data Structure
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes (in English)
Course Website (URL)	-

Learning Outcomes

Learning outcomes

The goal of this course is the deeper understanding of PROLOG. During the course a detailed examination of the following topics are done: Procedural and Declarative Programming Logic Programming a version of Declarative Programming The programming language PROLOG (PROLOG programs syntax, Lists, Operators, Arithmetic, Backtracking control, The negation in PROLOG, Recursive predicates, Data Structure manipulation, PROLOG implementation to searching problems, symbolic processing, natural language understanding and metaprogramming) Logic Programming Theory Logic Programming under restrictions Logic Programming systems implementation technics Parallel Logic Programming Logic Programming for knowledge representation. After completing the course the student can handle: theoretical documentation of problems solving exercises implementations-applications

General Competences

Applications Implementation- Consolidation

Syllabus

• Ιntroduction to Expert Systems
• Main Features of Expert Systems, classic examples
• Knowledge acquisition and verification, knowledge representation, inference and interpretation, consistency and uncertainties.
• Inference techniques
• Rule-based forward chaining Expert Systems
• Rule-based backward chaining Expert Systems
• Rule-based Expert Systems
• Expert Systems tools
• Users Interface
• Machine learning, decision making machines, Expert Systems examples.

Teaching and Learning Methods - Evaluation

Delivery	Face to face
Use of Information and Communications Technology	Yes , Use of Natural Language and Mathematical Problems Processing Laboratory
Teaching Methods	Activity Semester Workload Lectures 39 Self study 78 Exercises 33 Course total 150
Student Performance Evaluation	Final test

Attached Bibliography

• Γεώργιος Ι. Δουκίδης, Μάριος Κ. Αγγελίδης, "Έμπειρα συστήματα, τεχνητή νοημοσύνη και LISP", ISBN 960-08-0004-9, ISBN-13 978-960-08-0004-3
• Σπύρος Τζαφέστας, "ΕΜΠΕΙΡΑ ΣΥΣΤΗΜΑΤΑ ΚΑΙ ΕΦΑΡΜΟΓΕΣ", ISBN: - (Κωδικός Βιβλίου στον Εύδοξο: 89871)
• Παναγιωτόπουλος Ιωάννης - Χρήστος Π., "Νέες Μορφές Τεχνολογίας - Γενικευμένα Αυτόματα Συστήματα - Έμπειρα Συστήματα Turbo Prolog"