Course Tutors
LEARNING OUTCOMES
The objectives of this course are:
- understanding the basic concepts and applications of artificial intelligence
- familiarity with methods and mathematical models used to represent knowledge, expert systems, problem-solving techniques, inference techniques, and adversarial games.
- using the above methods for solving search problems, constraint satisfaction problems, adversarial games and implementing expert systems and knowledge representation
- developing skills related to the implementation of the above methods in a programming environment
Upon successful completion of the course the student will be able to:
- describe problems and represent relevant knowledge in formal ways
- distinguish the differences between blind and heuristic search algorithms and apply them in the context of problem solving
- know the different ways of representing knowledge
- design and develop adversarial decision-making systems
- understand the structure and operation of expert systems
- develop expert systems based on rules
General Competences
- Search, analysis and synthesis of data and information, using the appropriate technologies
- Individual work
- Project design and manipulation
- Work in an interdisciplinary environment
- Promotion of creative and inductive thinking
SYLLABUS
Historical review. Basic concepts. Knowledge representation. Blind search. Comparison of methods. Heuristic search techniques. A* Algorithm. Heuristic functions. Local search. Constraint satisfaction problems. Dissemination of restrictions. Problem solving. Reasoning techniques. Expert Systems. Knowledge and reasoning. Categorical logic. Introduction to Prolog. Development of artificial intelligence applications in Matlab programming environment.
STUDENT PERFORMANCE EVALUATION
I. Written final examination that includes:
– Short answer questions
– Problem solving
II. Midterm written examinations
III. Projects
The examination material and the evaluation process are announced to the students during the lectures and are also posted on the course’s website.
ATTACHED BIBLIOGRAPHY
In Greek
1. Ι. Βλαχάβας, Π. Κεφαλάς, Ν. Βασιλειάδης, Φ. Κόκκορας και Η. Σακελλαρίου, 2020, Τεχνητή Νοημοσύνη, Εκδόσεις Πανεπιστημίου Μακεδονίας.
2. S. Russell και P. Norvig, 2021, Τεχνητή Νοημοσύνη: Μια Σύγχρονη Προσέγγιση, Εκδόσεις Κλειδάριθμος.
In English
1. M. Tim Jones, 2008, Artificial Intelligence: A Systems Approach, Infinity Science Press.
2. N. J. Nilsson, 1998, Artificial Intelligence A New Synthesis, Morgan Kaufmann Publishers.