Introduction to Expert Systems (MAE846)

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Department of Mathematics
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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)	See eCourse, the Learning Management System maintained by the University of Ioannina.

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

See the official Eudoxus site or the local repository of Eudoxus lists per academic year, which is maintained by the Department of Mathematics. Books and other resources, not provided by Eudoxus:

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"