Department of Information & Communication Systems Engineering
University of the Aegean
SCHOOL OF ENGINEERING

Department of Information
& Communication Systems Engineering

Information & Communication Systems Security
Information Systems
Artificial Intelligence
Computer & Communication Systems
Geometry, Dynamical Systems & Cosmology
 
Τεχνητή Νοημοσύνη

Title: Τεχνητή Νοημοσύνη
Lesson Code: 321-3604
Semester: 6
ECTS: 5
Theory Hours: 3
Lab Hours: 2
Faculty: Stamatatos Efstathios
 
Content outline

Intelligent agents (basic concepts). Search in a state space for problem solving: Blind (but systematic) search, Guided search and heuristic methods, Search cost, Local search. Constraint satisfaction problems: Basic principles and algorithms. Planning: Basic principles and algorithms, Hierarchical planning. Machine learning: Introduction, Inductive learning, Machine learning algorithms.

 
Learning outcomes

Ability to define an intelligent agent and familiarity with the types of intelligent agents. Ability to represent a problem so that it can be solved via state space search. Familiarity with blind search algorithms. Familiarity with heuristic search algorithms. Understanding of the properties of heuristic functions. Familiarity with local search algorithms. Ability to represent a problem as a constraint satisfaction problem. Familiarity with algorithms of solving constraint satisfaction problems. Understanding of planning methods and the algorithm of partial-order planning. Familiarity with the basic principles and algorithms of machine learning. Ability to develop programs that use artificial intelligence algorithms.

 
Prerequisites

Not required.

 
Basic Textbooks

1. Russell and Norvig, Τεχνητή Νοημοσύνη: Μια Σύγχρονη Προσέγγιση, Κλειδάριθμος, 2005.
2. Βλαχάβας, Κεφαλάς, Βασιλειάδης, Κόκκορας, Σακελλαρίου, Τεχνητή Νοημοσύνη, Β. Γκιούρδας Εκδοτική, 2006.
3. David L. Poole and Alan K. Mackworth, “Artificial Intelligence: Foundations of Computational Agents”, Cambridge University Press, 2010.
4. M. Tim Jones, “Artificial Intelligence: A Systems Approach”, Infinity Science Press, 2008.

 

 
Additional References

1. Artificial Intelligence (Elsevier)
2. Journal of Artificial Intelligence Research
3. Intelligent Systems (IEEE)

 
Learning Activities and Teaching Methods

During the teaching hours of the course, teaching material is projected to highlight the characteristics of the examined methods and systems. Appropriate methods and algorithms are demonstrated. The active participation of students with critical questions and small-group discussions is strongly encouraged. Exercises are solved.

 
Assessment/Grading Methods

The grading of the students is based on two factors: the assessment of the final exam and the assessment of the laboratory exercises assigned to them during the semester.

 
Language of Instruction
Greek, English (for Erasmus students)
 
Μode of delivery

Face-to-face.



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