Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Artificial Intelligence - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
1 module among the following2 modules among the following2 modules among the following2 modules among the followingLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Planning and Automated Reasoning (2022/2023)
Teaching code
4S010674
Credits
12
Coordinator
Language
English
Also offered in courses:
- Foundations of Artificial Intelligence of the course Master's degree in Computer Science and Engineering
- Automated reasoning of the course Master's degree in Computer Science and Engineering
The teaching is organized as follows:
Learning objectives
The class presents the main techniques for problem solving, based on the central paradigm of symbolic representation, and then proceeds to illustrate selected methods for planning, theorem proving, and satisfiability testing. The objective is that the students learn to design, apply, and evaluate algorithms, procedures, and strategies for problems whose automated solution embodies fundamental aspects of artificial intelligence. At the end of the course the students must demonstrate to know and understand the main techniques for state space search, constraint solving, planning, theorem proving, and satisfiability testing, also modulo theories. Thus, the students will know how to choose the most appropriate solution techniques for different problems, and will be prepared to continue their studies in Artificial Intelligence.
Prerequisites and basic notions
Programming, algorithms, propositional logic and first-order logic, at the undergraduate level.
Bibliography
Criteria for the composition of the final grade
1st round: 25% PI + 25% L + 25% PF + 25% P where PI is the grade in the midterm exam (end of the 1st module), L is the grade for the lab activities during the 1st module, PF is the grade in the final exam (end of the 2nd module) and P is the grade in the project done during the 2nd module.
Later rounds: 100%E where E is the grade in the written exam.
