Introduction to Artificial Intelligence. Intelligent Agents. Blind Search, Heuristic Search, Local Search, Constraint Satisfaction Problems. Propositional Logic: Syntax and Semantics, Logical Inference, Proof Methods, Resolution. Predicate Logic: Syntax and Semantics. Planning: Basic Principles and Algorithms. Machine Learning: Inductive Learning, Decision Trees.

The expected learning outcomes are the following:

1. Understanding of basic concepts of Artificial Intelligence and Intelligent Systems.
2. Understanding methods of solving search problems in Artificial Intelligence.
3. Application of methods of solving to unknown problems.
4. Understanding of basic approaches to Knowledge Representation.
5. Develop experience in the implementation of search algorithms and logical inference.
6. Development of creative thinking.
7. Development of problem-solving skills.
8. Gain experience in collaborative management and problem solving.

Upon successful completion of the course, students will:

1. understand the basics of intelligent systems
2. know how to implement uninformed and informed search algorithms
3. are capable of reasoning in propositional logic
4. know the basic principles of action planning
5. understand the basics of machine learning

None

-

Written final exams (80%), Projects (20%)

• Russell & Norvig, Τεχνητή Νοημοσύνη: Μια Σύγχρονη Προσέγγιση, Κλειδάριθμος, 2004
• Βλαχάβας, Κεφαλάς, Βασιλειάδης, Κόκκορας, Σακελλαρίου, Τεχνητή Νοημοσύνη, Εκδόσεις Γαρταγάνης, 2005

- Recommended Article/Paper Resources:

Artificial Intelligence, Journal of Artificial Intelligence Research, Machine Learning, Journal of Automated Reasoning, Autonomous Agents & Multi-Agent Systems, AI Communications