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Artificial Intelligence

Description

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 state of the art 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 
  • For each topic you receive a paper to read and to support to your studies
    Simulated Annealing
    SA extension ILS: iterated local search
    Genetic Algorithms
    GA variant PBIL population-based incremental learning 
    Ant Colony Optimization
    ACS for vehicle routing problems
    Tabu search

 

EXAMINATION INFORMATION
Writen test and code evaluation

 

REFERENCES

  • 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

in addition the seven state of the art papers

Course Material in English will be provided to the students

People

 

Gambardella L. M.

Course director

Mele U. J.

Assistant

Additional information

Semester
Fall
Academic year
2021-2022
ECTS
6
Language
English
Education
Master of Science in Artificial Intelligence, Foundation course, 2nd year
Master of Science in Computational Science, Elective course, Lecture, 2nd year
Master of Science in Computational Science, Elective course, 1st year
Master of Science in Informatics, Elective course, 1st year
Master of Science in Informatics, Elective course, 2nd year