First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
Course unit
INO2044023, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
IN0521, Degree course structure A.Y. 2009/10, A.Y. 2018/19
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination INTELLIGENT SYSTEMS
Department of reference Department of Information Engineering
E-Learning website
Mandatory attendance No
Language of instruction Italian
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge MARIA SILVIA PINI ING-INF/05

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/05 Data Processing Systems 6.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 48 102.0 No turn

Start of activities 25/02/2019
End of activities 14/06/2019
Show course schedule 2019/20 Reg.2009 course timetable

Examination board
Board From To Members of the board
7 A.A. 2019/2020 01/10/2019 15/03/2021 PINI MARIA SILVIA (Presidente)
GHIDONI STEFANO (Membro Effettivo)
NANNI LORIS (Supplente)
VANDIN FABIO (Supplente)
6 A.A. 2018/2019 01/10/2018 15/03/2020 PINI MARIA SILVIA (Presidente)
NANNI LORIS (Membro Effettivo)
VANDIN FABIO (Supplente)
5 A.A. 2017/2018 01/10/2017 15/03/2019 BADALONI SILVANA (Presidente)
MORO MICHELE (Membro Effettivo)

Prerequisites: It is recommended to have basic knowledge of Programming and Algorithms.
Target skills and knowledge: Learning the fundamental techniques of some significant approaches within Artificial Intelligence for the solution of difficult problems. In particular, knowledge of research techniques in a space of solutions, systems with constraints, soft constraints, planning techniques, representation and manipulation of knowledge with and without uncertainty, decision theory, reasoning techniques with preferences and aggregation of preferences in a multi-agent context even in the presence of uncertainty.
Ability to develop autonomously or in group an application project on a specific case study.
Examination methods: The student must pass a written examination that contains questions and exercises regarding the main techniques of Artificial Intelligence. In addition, he must develop an application project agreed with the teacher, possibly together with other students.
Assessment criteria: Student evaluation is based on a verification of the learning of the basic concepts introduced during the course and on the student's analytical ability.
The project evaluation considers the student's ability to identify an adequate case study and to carry out in group or autonomously an activity of planning and realization of appropriate quality.
Course unit contents: The structure and the topics of the course will be as follows:
- Introduction and some hints of intelligent agents architectures;
- Problem resolution, constraint-based systems and soft constraints;
- Planning;
- Treatment of uncertainty and probabilistic reasoning;
- Decision theory;
- Preference reasoning and preference aggregation in multi-agent systems also in presence of uncertainty.
Planned learning activities and teaching methods: The activities include hours of classroom and laboratory lessons. In classroom lessons the theoretical contents of the course are presented. The laboratory lessons present specific software, that students must use to solve the assigned exercises.
Additional notes about suggested reading: All material related to classroom and laboratory lessons is made available on the moodle platform.
Textbooks (and optional supplementary readings)
  • Stuart Russell, Peter Norvig, Artificial Intelligence: A modern approach, Third Edition. --: Prentice Hall, 2010. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Case study
  • Working in group
  • Use of online videos
  • Loading of files and pages (web pages, Moodle, ...)
  • Learning journal
  • Reflective writing

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)