OVERVIEW

The course Philosophy and Aritficial Intelligence provides an introduction to a number of fundamental methods in AI, it approaches their logical, epistemological, and semantic foundations, and introduces a number of topics in the contemporary debate in Philosophy of AI. 

AIMS AND CONTENT

LEARNING OUTCOMES

The course introduces a number of methods in AI and it discusses its foundations. After dicussing the basis of Computability Theory and Computational Complexity, the course focuses on Knowledge Representation, in particular, by focussing on the connection with philosophical disciplines such as Logic, Epistemology, Ontology, and Semantics.

AIMS AND LEARNING OUTCOMES

The course introduces the main methods of AI and it analysis its foudnations and philosophical implications. 

The objectives of the course are

• Introducing the basics of Computability and of Computational Complexity;
• Introducing the fundamental methods in Symbolic AI;
• Analysing the logical, epistemological and semantical foundations;
• Introducing methods for Knowledge Representation (Computational Ontologies, Description Logics);
• Introducing a number of elements of AI based on Machine Learning and analysing its epistemological foundations;
• Introducing the debate on the status of AI
• Focusin on a number of specific topics in the current debate on Philosophy of AI.

At the end of the course, student are expected to:

• Understand the main concepts of Computability and Computational Complexity. 
• Understand the AI methods that have been introduced;
• Get familiar with the Knowledge Representation techniques:
• Understand the main points of the debate on the status of AI;
• Understand, explain, and summarise research texts in Philosophy of AI; 
• Apply philosophical methods to the discussion of problems in Philosophy of Ai;
• Improve the capacity of interacting collaboratively, communicate constructively, and improve dialogical skills.
• Approach the proposed problems with creativity.
• Show automony, capacity of confronting primary literature, argumentative capacity, collaborative attitude, coordination and negotiation.

PREREQUISITES

It is advisable that students have already attended an introductory course of Logic.

TEACHING METHODS

The course is divided into two parts. 

The first part providdes lectures and moments of exercise and application (individually and in groups).

The second part consists of students' presentation, projects and discussions in class. 

It is imporant to register to Aulaweb, where you may access the teaching materials.

SYLLABUS/CONTENT

The course is divided into two parts.

The first part introduces the main methods and topics in AI.

• Algorithms, computability, complexity; 
• Logic and Knowledge Representation
• Computaional Ontologies and Description Logics; ;
• AI based on Machine Learning and its epistemological foundation;
• Overview of the debate on AI (e.g. strong, weak, general AI)

The second part introduces a number of specific topics and research texts in AI and in Philosophhy of AI. 

RECOMMENDED READING/BIBLIOGRAPHY

Attending students

1)  Lecture notes and other teaching material that will be made available on Aulaweb.

2)  A paper for classroom presentation chosen among those that will be made available on Aulaweb.

3) S. Russel e P. Norvig, "Artificial Intelligence: A modern approach", Global Edition, Pearson. (selected chapters)

NON-attending students: besides the material listed above, the students have to contact the teachers to decide the program. 

TEACHERS AND EXAM BOARD

Exam Board

MARCELLO FRIXIONE (President)

DANIELE PORELLO (President Substitute)

MARIA CRISTINA AMORETTI (Substitute)

LESSONS

LESSONS START

26th February 2025

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Attending students: classroom presentation and oral examination. Schedule and modality of the classroom presentations will be planned during the course.

NON-attending students: oral examination.

The registration for the examination is mandatory and must be done at least one week before the exam.

ASSESSMENT METHODS

Attending students

- the classroom presentation (15 points out of 30) assesses the student’s ability to understand, synthesize and expose a philosophical text, and to apply the tools of philosophical reasoning in the discussion of problems related to AI;

- the oral exam (15 points out of 30) assesses the student’s ability to understand, retain, explain and apply philosophical concepts and arguments concerning AI

NON-attending students

- the oral exam assesses the student’s ability to understand, retain, explain and apply philosophical concepts and arguments concerning AI.

In both cases, the correct use of the philosophical lexicon, the quality of the exposition, as well as the capacity for critical and argumentative reasoning and the creative approach to problem solving will be taken into account.

Exam schedule

FURTHER INFORMATION

Students that do not attend classes are required to get in touch with the professor.