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Computer Science
First semester
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Learning outcomes of the course unit

The course starts from the basics of classic decision processes. It then passes through the study of languages and tools. It eventually ends to provide the basic of foundational aspects of intelligent agents.
The course is structured into frontal lessons and offline exercizes.

Taking Dublin Indicators into account:

Knowledge and understanding
The course introduces the first concepts related to intelligent agents. Particular emphasis is given to the understanding of the classical techniques. English terminology is commonly used during lessons as goodwill to the consultation of international scientific literature.

Applying knowledge and understanding
The knowledge presented is always applied to the solution 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 the students the ability to structure procedures that are useful in many parts of Artificial Intelligence.

Making judgments
The exercises, which are proposed in relation to the theoretical part presented in classes, 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, exercises can be solved in different ways and the listening to the solutions proposed by others 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 Artificial Intelligence concepts 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.

Course contents summary

Introduction to intelligent agents.
Insights on selected topics of probability theory.
Introduction to the analysis of rational decision processes in observable and partially observable environments.
Introduction to the analysis of multi-agent decision processes in observable and partially observable environments.
Introduction to software agent programming and related languages and tools.
Introduction to multi-agent programming and related languages and tools.

Recommended readings

Didactic material provided by the teacher.

Teaching methods

Classes are held at the Campus of Science 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 Artificial Intelligence.
The exam consists of a written test. An offline project follows the written test only when the written test is sufficient.
An oral session is requested when the assigned project is sufficient.