Intelligent Systems 17/18

This web site is dedicated to the Intelligent Systems M course. The web site aims at providing the students with access to information related to the goals, contents, texts and assessment criteria of the course.

Course schedule: see the information system of the School of Engineering.

Course professor:
Prof. ssa Michela Milano, Tel. 051 20 93790, michela <dot> milano <at> unibo <dot> it
Office hourse: Thursday from 10 to 12 (ex-CSITE building, above lecture room 8.1)

Teaching Assistant:
Dott. Michele Lombardi, Tel. 051 20 93270, michele <dot> lombardi2 <at> unibo <dot> it
Office hours: Tuesday from 10 to 12, on appointment to be booked by email ("Aule Nuove" building, close to lecture room 5.7) 



The course takes advantage of some of the topics previously discussed in the Artificial Intelligence course (year 1), such as knowledge representation, logic, informed search strategies, game theory, constraint resolution. The Intelligent System course starts from such bases and aims at presenting the main applications of Artificial Intelligent methods, with practical examples.

The main goals of the course are:


Some interesting links


Program and Evaluation Process


The course features both regular lectures and lab sessions. For each of the considered topics, a lab session will be scheduled after the lectures, so as to provide a chance to learn the software systems related to the presented topic. The techniques discussed in this course represent the state of the art of scientific research in Artificial Intelligence: for each of the consdidered topic, the students will be referred to survey paper that provide a good overview of state of the art research.

The detailed program is as follows:

  1. Planning
    1. Non linear planning
    2. Hierarchical planning
    3. Graph-based planning 
  2. Swarm Intelligence
    1. Ant-colony Optimization
    2. Particle Swarm Optimization
  3. Constraint Programming and Optimization
    1. Applucations
    2. Advanced Search and Propagation Strategies
  4. Machine Learning
    1. Decision Trees
    2. Neural Networks

Assessment methods

The final exams consts of a written test containing both exercises and questions about theoretical topics. For this reason, it will not be possible to bring notes and books to the test.

Optional Course Project

It is possible to opt (by formally requesting it when planning the courses to take) for a course project in Intelligent Systems. In such a case, the project topic should be defined together with the course teacher.

The project may involve using an existing system to solve a complex problem , or the development of a new tool to solve for an AI application. The student should provide:

  • An accurate report about the project contents and the developed code
  • A presentaion (i.e. slides) to summarize the main steps of the projec, which will serve as a basis to guide the discussion
  • The project code


Books & Papers


 Reference Books

About AI in general:


Additional books:

Scientific Papers

Articoli scietifici relativi ai contenuti del corso verranno pubblicati con l'avanzare del programma.


Swarm Intelligence:

Neural Networks:

Constraint Programming:


Lectures, Lab & Seminars


Lecure slides will be updated as the course progresses:

Lab sessions

Slides and data files will be published as the course progresses 


Slides will be published as the course progresses

On the integration of AI and Robotics (Alessandro Saffiotti)


Predictive and Prescriptive analytics: an industrial perspective (Alession Bonfietti, MindIT).

Room 4.1 - Tuesday June 12 - 10,30

Talk of artificial intelligence is everywhere. This workshop is designed to help technologists, scholars, and students understand how state-of-the-art AI technologies can be applied with success to real world problems. Ranging from Predictive Maintenance where, nowadays, a solution able to predict  when maintenance should be performed helps to reduce maintenance costs and time, to Retail Promo Optimization where useful advices can be provided to retailers to improve their promotion effectiveness, the talk will present both the point of view of the scientist and the user.

The MindIT team is composed by mathematicians and computer engineers with an extensive experience in machine learning and optimization techniques and algorithms. The team has been created at the University of Bologna in 2008. Members of the team have won four national and international (from Google) awards for "research in AI”.

Exam Exercises

Full exam text from the former course "Applicazioni di Intelligenza Artificiale"

WARNING: these are all in Italian!

WARNING: the constraint programming exercise has changed radically since then. Hence, don't pay attention to the CP exercises!

Some Constraint Programming exercises from the course "Sistemi Intelligenti"

WARNING: these are all in Italian!

Complete exam texts from the course "Sistemi Intelligenti"

WARNING: these are all in Italian!

Exam texts from the course "Sistemi Intelligenti", no solutions

WARNING: these are all in Italian!

Complete exam texts from the course "Intelligent Systems"

Some of the Constraint Programming exercises have been presented during the course.