Artificial Intelligence and laboratoryModule Artificial Intelligence
Academic Year 2024/2025 - Teacher: MARIO FRANCESCO PAVONEExpected Learning Outcomes
Knowledge and understanding: Students will acquire basic knowledge about Intelligent Agents and their main features.
Applying knowledge and understanding: students will be to able to apply the acquired knowledge in several fields such as: searching for solutions to hard combinatorial problems, games and decision theory, automated deduction and reasoning.
Making judgements: Students will be able to evaluate the possibility of developing algorithms and intelligent systems to mechanize decisional processes in different application fields.
Communication skills: students will acquire the necessary communication skills and appropriate linguistic skills to explain and clarify problems relative to intelligent systems and their applications.
Learning skills: students will be able to adapt the acquire knowledge to new contexts as well and to understand the limits of applicability of artificial intelligence techniques
Course Structure
Classroom-taught lessons.
Can be also included external seminars held by expert researchers on related topics.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Learning assessment may also be carried out on line, should the conditions require it.
Required Prerequisites
Attendance of Lessons
Detailed Course Content
The course is divided into 2 main parts. First part on Problem Solving, and second part on Knowledge and Reasoning.
FIRST PART: Problem Solving
- Foundations and history of Artificial Intelligence
- Intelligent Agents and classifications
- Search and Problem Solving
- Search in games
- Constraint Satisfaction Problems
- Search using Natural Computing Algorithms
SECOND PART: Knowledge, Reasoning and Learning
- Logical agents and puzzles
- First order logic and Inferences
- Uncertainty and Probability
- Decision making, Utility and value of information
- Learning from examples
Textbook Information
Required textbook is Artificial Intelligence, a modern approach, 3rd Edition, S. Russel, P. Norvig. Other material will be provided by the instructor in class.
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Fondamenti e Storia dell'Intelligenza Artificiale | Cap. 1 e 27 |
| 2 | Agenti Intelligenti | Cap. 2 |
| 3 | Risoluzione dei problemi per mezzo di ricerca | Cap. 3 |
| 4 | Oltre la ricerca classica | Cap. 4 |
| 5 | Ricerca con avversari e giochi | Cap. 5 |
| 6 | Problemi con soddisfacimento di vincoli | Cap. 6 |
| 7 | Agenti Logici | Cap. 7 |
| 8 | Logica del primo ordine | Cap. 8 |
| 9 | Inferenza nella logica del primo ordine | Cap. 9 |
| 10 | Quantificare l'incertezza | Cap. 13 |
| 11 | Decisioni Semplici | Cap. 16 |
| 12 | Apprendimento da esempi | Cap. 18 |
Learning Assessment
Learning Assessment Procedures
The evaluation is based on an oral interview on all topics of the program. In addition, students will present a project (algorithm) on one of the topics involved during the lectures.
The learning assessment may also be carried out electronically, if conditions require it.
Students with disabilities and/or DSA must contact the teacher, the CInAP representative of the DMI and CInAP well in advance of the exam date to communicate that they intend to take the exam using the appropriate compensatory measures.