Artificial Intelligence

COURSE OBJECTIVES
Meta-Heuristics algorithms learning and testing

COURSE DESCRIPTION
The aim of the course is to present the most modern techniques for solving complex problems. We focus on meta-heuristic for continuous function and combinatorial optimization: among the methods we deepen simulated annealing, genetic algorithms, variable neighborhood search and ant colony optimization. Gaming with two players will be also discussed as the integration between meta-heuristics and machine learning. Students are asked to implement and test some of these techniques.

LEARNING METHODS
Front lecture, text reading, code development

EXAMINATION INFORMATION
Mid terms, Final written exam

REFERENCES
Required Materials

• Artificial Intelligence: A Modern Approach (3rd Edition) by Stuart Russell, Peter Norvig
• Artificial Intelligence, third edition, P.H. Winston, Addison-Wesley
• Genetic Algorithms in Search, Optimisation, and Machine Learning, Goldberg, Addison-Wesley, MA
• Ant Colony Optimization By Marco Dorigo and Thomas Stützle A Bradford Book Course
• Material in English will be provided to the students