75943 - Artificial Intelligence, Problem solving and the Semantic Web (1)

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Industry, innovation and infrastructure Partnerships for the goals

Academic Year 2021/2022

Learning outcomes

The aim of the course is to develop the principal aspects of artificial intelligence in relation with cognition, semantics and communication studies, and to convey the main positions within the current cognitive science. At the end of the course, students will be able to use the basic elements of artificial intelligence in the field of new communications technologies and to orientate themselves among the different disciplines constituting or related to artificial intelligence.

Course contents

The course deals with the main topics of artificial intelligence: problem solving, knowledge representation, planning, machine learning, natural language processing, semantic web, ontologies, human-computer interaction, also in connection with the most recent trends in robotics. Topics will be treated in the framework of contemporary communication practices through digital devices.

This is a basic course and does not require previous knowledge of the fields related.

Relevant topics of the course will be the evolution of the notion of artificial intelligence through XX and XXI centuries, with special reference to rationality, decision theory, language understanding, semantic web, related philosophical subjects, cognitive modeling, psychological framework of research, different methodologies of studying mental processes, neuroscience debate, and robotics in connection with AI and cognitive science.

The course is temporally divided into three parts:

1) Main topics of artificial intelligence

2) Problem solving and automated planning

3) Knowledge representation and semantic technologies

Readings/Bibliography

Mandatory textbook:

- S. Russell, P. Norvig, Intelligenza artificiale. Un approccio moderno, volume 1, Pearson-Italia, Milano-Torino, 2010 (Chapters 1, 3, 4, 7, 8, 9, 10, 11, 12, 26, 27) [The English version is also available.]

- F. Bianchini, A. Gliozzo, M. Matteuzzi (a cura di), Instrumentum vocale. Intelligenza artificiale e linguaggio, Bononia University Press, Bologna, 2007 (capitoli 1, 2, 3, 5, 6, 7).

Suggested readings:

- M. Marraffa, Percezione, pensiero, coscienza. Passato e futuro delle scienze della mente, Rosenberg & Sellier, Torino, 2019.

- R. Pieraccini, The Voice in the Machine. Building Computers That Understand Speech, MIT Press, Cambridge, Mass. 2012.

- K. Warwick, Intelligenza artificiale. Le basi, Flaccovio Editore, 2015.

Further suggested readings will be mentioned during the course.

Not-attending students are required to study one more book among the suggested readings.

Teaching methods

Lectures. Debates on main topics. Personal or group presentations on a subject agreed with professor will be possible.

Assessment methods

Student will be tested through an oral examination in which s/he will face general subjects of artificial intelligence and specific subjects of fields involved in this disciplinary approach. The knowledge of the topics of the course and the capability of using them autonomously will be taken into consideration, together with the ability in producing personal remarks on the contents developed during the lessons. If it will be possible, students will be required to apply the main concepts in simple exercises.

More specifically, the achievement of the following targets will have a growing weight:

1) the completeness of the basic knowledge strictly connected to the program;

2) the appropriateness of the specific language;

3) the capability of personally re-using concepts learned during the course;

4) the capability to manage interdisciplinary reflections and argumentations;

5) the capability to apply the subjects to specific research cases and to produce autonomous and original remarks.

1) and 2) are the lowest targets for the pass mark. 3) could give a fair evaluation, the more being so the less the learned knowledge will be mnemonic. 4) is for a good outcome, 5) for an excellent one.

Teaching tools

Slides and digital contents will be used during lectures.

Office hours

See the website of Francesco Bianchini