Course Unit Page

Academic Year 2019/2020

Learning outcomes

The goal of the course is to introduce the artificial intelligence subfield of knowledge representation with emphasis on technological and practical aspects. Standard languages and tools for building knowledge based systems will be introduced, considering knowledge sharing and reuse techniques and Semantic Web technologies.

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 oral examination

Teaching tools

We will use slides and specific software tools  for AI applications

Links to further information


Office hours

See the website of Maurizio Gabbrielli