Logics for Ai

A.Y. 2024/2025
6
Max ECTS
48
Overall hours
SSD
M-FIL/02
Language
English
Learning objectives
By studying some of the most relevant applications of logic to AI systems, students will gain knowledge of their use and applications. The course includes activities for understanding the use of logics for knowledge representation and reasoning under uncertainty and with defeasible rules, verification, single and multi-agent information models, and for formal analyses of trustworthy and fair AI systems. Such notions and methods will be valuable in any activity requiring advanced reasoning and problem-solving abilities in the computational domain with particular reference to AI.
Expected learning outcomes
The course provides knowledge of formal methods and logics in the area of computing, information transmission, knowledge representation and verification, trustworthiness essential for the analysis and understanding of AI systems. The course provides basic knowledge and technical skills in the following topics:

- Logics of Program Correctness
- Process Algebra & Temporal Logics
- Multi-Agent Systems

- Reasoning under Uncertainty

- Trustworthy AI

Skills acquisition and ability to apply knowledge:

At the end of the course, students are expected to be able to:

- formally express reasoning about computational processes;
- formally describe informational structures and reason on them.

- formally model interacting systems under uncertainty and with defeasible rules

- formally define desirable properties of computational systems like fairness and trustworthiness.
Single course

This course can be attended as a single course.

Course syllabus and organization

Single session

Responsible
Lesson period
Second semester
Course syllabus
- Propositional Logic
- Modal and Multi-modal Logic
- Temporal Logics
- Logics for Data, Bias and Trust


Moodle Site: https://myariel.unimi.it/course/view.php?id=3580
Prerequisites for admission
Knowledge of Propositional and First-Order Logic is advised.
Teaching methods
Frontal lectures, flipped-class exercises and tests.
Teaching Resources
Handouts by the Lecturer.
Additional material:
R.J. Brachman, H.J. Levesque. Knowledge Representation and Reasoning. MK, 2004. [selected chapters]
M.Huth, M.Ryan, Logic in Computer Science, CUP 2004. [selected chapters]
C. Bayer, J.P. Katoen, Principles of Model-Checking, MIT Press, 2008. [selected chapters]
G.Primiero. On the Foundations of Computing. OUP, 2019. [selected chapters]
Selection of scientific papers.
Assessment methods and Criteria
For attending students:
- flipped classroom exercise
- midterm assessment test
- written paper
In case of missing or failing on at least one test, written exam at the end of the course with multiple-choice and open-ended questions to test understanding of concepts and definitions, with exercises formulated to assess problem-solving ability.

Non-attending students: written exam at the end of the course with multiple-choice and open-ended questions to test understanding of concepts and definitions, with exercises formulated to assess problem-solving ability.
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE - University credits: 6
Lessons: 48 hours
Professor: Primiero Giuseppe
Professor(s)
Reception:
Tuesdays, 14:00-17:00. Students are kindly asked to get in touch by email to confirm date and hour.
Teams/Slack