The main objective of the course is to provide students with a solid understanding of the fundamental concepts and techniques of knowledge representation, reasoning, problem-solving, and planning in Artificial Intelligence. The course introduces classical and advanced search techniques, adversarial search, constraint satisfaction problems, logical agents, first-order logic, automated inference, and planning, with particular emphasis on how knowledge can be represented and used to support intelligent decision-making. The expected learning outcomes include knowledge of the main problem-solving and search techniques used in Artificial Intelligence, understanding of adversarial search and constraint satisfaction problems, and knowledge of the principles of knowledge representation and logical agents. Students will be able to represent knowledge using propositional and first-order logic, derive logical consequences using inference techniques, formulate and solve planning problems, and analyse and compare different approaches to problem-solving, reasoning, and planning. They will also be able to apply knowledge representation and reasoning techniques to Artificial Intelligence problems and applications.
Prerequisiti
Basic notions of algebra, logic, and set theory
Metodi didattici
The course consists of traditional lectures aimed at presenting and discussing the main theoretical concepts, complemented by guided exercises carried out at the board. The exercises will provide students with the opportunity to apply the concepts introduced during the lectures, analyse problems, and discuss possible solution strategies.
Verifica Apprendimento
The examination consists of a compulsory written test including multiple-choice questions and exercises covering the different topics addressed during the course, including the practical programming exercises. Each question is assigned a score of 1, 2, or 3 points, depending on the correctness and completeness of the answer provided by the student. The final grade is calculated as the sum of the scores obtained on the individual questions and exercises. The examination is passed only if a minimum score of 18 is achieved. A score of 31 is required to obtain honors (cum laude).
Testi
Artificial Intelligence: A Modern Approach Stuart Russell and Peter Norvig. Pearson, 4th Edition, 2021.
Contenuti
The course introduces the fundamental concepts and techniques of Artificial Intelligence for problem-solving, knowledge representation, reasoning, and planning. It covers problem-solving through classical and advanced search, adversarial search, and constraint satisfaction problems. The course then focuses on knowledge and reasoning, including logical agents, first-order logic, inference in first-order logic, classical planning, planning and acting in the real world, and knowledge representation.
