Course Unit Page

Academic Year 2020/2021

Learning outcomes

At the end of the course the student will know the principal languages, the modeling techniques and the reasoning methods that are at the base of artificial intelligence. In particular the student will be able to construct systems that exhibit intelligent behaviours, often simulating the behavior of human experts of a specific discipline. Moreover she will be able to model and solve simple constraint and optimization problems by using constraint programming.

Course contents

Introduction to artificial intelligence.
The principal technologies and applications of artificial intelligence.
The notion of agent.
Non informed search strategies.
Informed search strategies.
Search with adversaries.
Modeling of problems with costraints and CSP: basic notions.
Notions of local consitency.
Propositional logic and first order logic (basics notions).
Unification. Resolution and inference.
Logic programming.
Constraint programming, basic notions of MiniZinc.
Introduction to machine learning.

Sub-symbolic computation and neural networks .
Philosophical aspects and future challenges.


Russell, Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition. Pearson (Intl) 2010 (US edition) and 2016 (Global edition).

Handouts provided during the course.

Teaching methods

Frontal lessons.

Assessment methods

Project and wirtten or oral exam.

Teaching tools

We will use slides and specific software tools for AI applications

Office hours

See the website of Maurizio Gabbrielli