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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.
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.
|Stuart Russell, Peter Norvig||Artificial Intelligence: A Modern Approach (Edizione 2)||Prentice Hall||2003||0137903952|
The final grade for the IA module can be achieved with a single test or with partial tests. The single written test will be done at the exam date. The partial tests includes a written test (done during the course) and an assignment. The assignment can be a project (usually with a consistent programming part) or a seminar (about 40 mins) given by the student.
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