34781 - Artificial Intelligence (2nd cycle)

Course Unit Page

  • Teacher Andrea Roli

  • Learning modules Andrea Roli (Modulo 1)
    Stefano Benedettini (Modulo 2)

  • Credits 9

  • SSD ING-INF/05

  • Language Italian

  • Campus of Cesena

  • Degree Programme Second cycle degree programme (LM) in Computer Engineering (cod. 8200)

Academic Year 2010/2011

Learning outcomes

The course aims at providing students the fundamentals of Artificial Intelligence (AI). Students will be able to tackle search problems using classical AI algorithms, such as uninformed tree-search algorithms, Best First Greedy and A*, as well as constraint satisfaction and constrained optimization problems by means of backtracking search, consistency techniques, constraint programming and metaheuristics. Moreover, students will know the basics of evolutionary computation, swarm intelligence techniques and neural networks. As far as knowledge representation and automatic reasoning is concerned, students will learn how propositional and first order logics are used in expert systems and classical planning techniques.

Course contents

  • Introduction
    • Foundations of AI
    • History of AI
    • Application domains of AI
  • Problem solving
    • Solving Problems by Searching
    • Non-informed and informed Search and Exploration
    • Constraint Satisfaction Problems (complete and incomplete techniques: standard backtracking, constraint propagation techniques, local search)
    • Adversarial Search: two players games, games with uncertainty
  • Decision support systems and technologies
    • Knowledge representation
    • Inference in propositional and first order logic
    • Reasoning
  •     Planning
    • Introduction to planning and scheduling problems
    • Planning and scheduling solving techniques (basics)
  • Machine learning
    • Reinforcement learning
    • Evolutionary computation
    • Neural networks

Readings/Bibliography

Russell, Norvig, "Artificial intelligence: A modern approach", Vol.1 and 2 (partially), second edition, Pearson/Prentice Hall

Teaching methods

  • Lectures in classroom
  • Lectures in lab (AI software tools)

Assessment methods

Written and oral examination.

Teaching tools

  • Lecture slides (in English) and web resources
  • AI software tools

Links to further information

http://www.lia.deis.unibo.it/~aro/

Office hours

See the website of Andrea Roli

See the website of Stefano Benedettini