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Introduction to Expert Systems (MAE846)

Introduction to Expert Systems (MAE846)

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Undergraduate Courses Outlines - Department of Mathematics

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

See the official Eudoxus site or the local repository of Eudoxus lists per academic year, which is maintained by the Department of Mathematics. Additionally:

  • 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"
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