cod. 06149

Academic year 2021/22
1° year of course - Second semester
- Federico BERGENTI
Academic discipline
Informatica (INF/01)
Attività formative affini o integrative
Type of training activity
48 hours
of face-to-face activities
6 credits
course unit

Learning objectives

Provide an introduction to modern Artificial Intelligence (AI) with particular regard to the various forms of automated reasoning.

Taking Dublin indicators into account.

Knowledge and understanding
The course introduces the first concepts related to AI.
Particular emphasis is given to the understanding of the classical methodologies. The reference text is in Italian, but standard English terminology is commonly used during the lessons as goodwill to the consultation of the international scientific literature.

Applying knowledge and understanding
The knowledge presented is always applied to the resolution of specific problems. The exercises that accompany the course are focused on solving exercises and problems. Often the solution methods are presented in the form of an algorithm, developing in students the ability to structure procedures that are useful in many parts of computer science, and not
only in the study of AI.

Making judgments
The exercises, which are proposed in relation to the theoretical part presented in class, can be solved individually or in groups. The comparison with classmates, work at home or in classroom, favors the development of specific skills in students to enable the explanation of arguments to fellows and teachers. Often the exercises can be solved in many different ways and listening to the solutions proposed by other allows students to develop the ability to identify common structures, beyond the apparent superficial differences.

Communication skills
The numerous discussions on the different methods to solve problems allow students to improve communication skills. Specific communication of AI is also usually used during classes and exercises.

Learning skills
The study of the origins of technological solutions and their introduction motivated by qualitative and quantitative considerations contributes to the students’ ability to learn in a deep way and not just superficial and repetitive. The knowledge acquired is never rigid and definitive, but it is adaptable to any evolution and change of perspective and context.


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Course unit content

Artificial intelligence and agents.
Problem solving via search.
Games and adversarial problems.
Constraint satisfaction problems.
Logic-based agents.
Structured knowledge representation.
Machine learning.
Neural networks.
Multi-agent systems.

Full programme

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Stuart Russell e Peter Norvig. Intelligenza artificiale: un approccio
moderno. UTET Libreria, 1998.

Teaching methods

Classes are located at the Campus of Sciences and Technologies. Meetings with the teacher can be requested via e-mail.

Assessment methods and criteria

Being able to understand and make appropriate use of techniques of
modern Artificial Intelligence.
The exam consists of a written test.
An oral session can be requested by students whose written test is sufficient.

Other information

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