Learning outcomes

The class presents the main techniques for problem solving, based on the central paradigm of symbolic representation. The objective is to provide the students with the ability to design, apply and evaluate algorithms for difficult problems, meaning that their mechanical solution captures aspects of artificial intelligence or computational rationality.

Program

Problem solving as search in a state space; un-informed search
procedures; heuristic search procedures; adversarial search.
Problem solving based on constraint processing (satisfaction and optimization).
Logic-based knowledge representation: normal forms; equality. Theorem proving: satisfiability (SAT),
resolution, rewriting. Intelligent agents: planning, multi-agent systems, coordination.
Probabilistic reasoning, decision theory.

Examination Methods

The final grade is given by 50% written test + 50% assignment. The written test will focus on exercises or on general questions about techniques studied during the course. The assignment can be a project (usually with a consistent programming part) or a seminar (about 40 mins) given by the student. Partial test mode: it applies only to the exam sessions right at the end of the class. The written test is done during the course (about half of the course) and the assignment can be done in collaboration with another person. Single test mode: written test is done the date of the exam assignment must be individual.