Artificial Intelligence (2007/2008)

Course partially running

Course code
Name of lecturer
Maria Paola Bonacina
Number of ECTS credits allocated
Academic sector
Language of instruction
1° Q dal Oct 3, 2007 al Dec 4, 2007.
Web page

Lesson timetable

1° Q
Day Time Type Place Note
Monday 4:30 PM - 6:30 PM lesson Lecture Hall D  
Tuesday 10:30 AM - 11:30 AM lesson Lecture Hall B  
Wednesday 4:30 PM - 6:30 PM lesson Lecture Hall L  

Learning outcomes

The class introduces the student to the field of Artificial Intelligence, presenting its problems, concepts and basic methods. After learning the general framework of automated problem solving with search, one studies problems and techniques of Artificial Intelligence in selected areas, such as constraint problem solving, games, planning, automated reasoning and machine learning. After the class the student is ready to work on an MS thesis in Artificial Intelligence.


Methods of artificial intelligence: problem solving as search in a state space; un-informed search procedures; informed search procedures and heuristic search. Constraint problem solving. Adversarial problems: games. Knowledge Representation: usage of logics (e.g., propositional, first-order) to represent knowledge. Automated reasoning in propositional logic: the Davis-Putnam-Logemann-Loveland procedure. Automated reasoning in first-order logic: forward and backward reasoning; resolution. Planning. Machine learning.

Reference books
Author Title Publisher Year ISBN Note
Elaine Rich, Kevin Knight Artificial Intelligence (Edizione 2) McGraw Hill 1991 0070522634 Altro libro di riferimento per intelligenza artificiale.
Stuart Russell, Peter Norvig Artificial Intelligence: A Modern Approach (Edizione 2) Prentice Hall 2003 0137903952 In alternativa al testo adottato.
David Poole, Alan Mackworth, Randy Goebel Computational Intelligence -- A logical approach (Edizione 1) Oxford University Press 1998 0195102703 Altro libro di riferimento per intelligenza artificiale.
Klaus Truemper Design of Logic-based Intelligent Systems (Edizione 1) John Wiley and Sons 2004 0471484032 Altro libro di riferimento per intelligenza artificiale.
Judea Pearl Heuristics: Intelligent search strategies for computer problem solving (Edizione 1) Addison Wesley 1985 0-201-0559 Altro libro di riferimento per tecniche di ricerca.
Stuart Russell, Peter Norvig Intelligenza artificiale: Un approccio moderno (Edizione 2) Pearson Education Italia 2005 88-7192-22 Testo adottato.

Assessment methods and criteria

Partial-tests mode:
this mode applies only to the first exam session after the end of the class, that for the December session, since the class is offered in the Fall term. The exam consists of a written test (C) and an individual programming project (P) to be developed at home or in the lab during the class; the final grade is given by: 50% C + 50% P; after the December session, partial tests have no value whatsoever.

Single-test mode:
in this mode, the exam consists of a single written test (E), whose difficulty is such to match that of C + P, and whose grade alone determines the final grade. This mode applies to all sessions including the first one after the end of the class. However, the student who takes the test E in the December session loses the grade matured with 50% C + 50% P.

Notes: written test C (partial test) will be held in the same date, time and place as test E of the December session (of course, contents and duration of C and E will be different).

Registration: for each session, the date of the exam is the date of the written test E and it is sufficient to register for that date. Students are not allowed to "reject" the grade and all grades will be registered. Students dissatisfied with their performance may withdraw: in order to withdraw it is sufficient not to hand-in E or C.

Rules: it is strictly forbidden to copy, exchange or share code or keys to the questions. All coursework showing signs of cheating will receive grade 0 with no distinction between those who copy and those who let copy.