General objectives:
Acquire the basic principles of the field of Artificial Intelligence, specifically the modeling of intelligent systems through the notion of intelligent agent.
Acquire the basic techniques developed in the field of Artificial Intelligence, concerning symbol manipulation and, more speicifically, discrete models.
Specific objectives:
Knowledge and understanding:
Automated search in the space state: general methods, heuristic driven methods, local Search. Factored representations: constraint satisfaction problems, automated planning.
Knowledge Representation through formal systems: propositional logic, first order logic, description logic (hints), non monotonic reasoning (hints). Usage of logic as a programming language: PROLOG.
Applying knowledge and understanding:
Modeling problems by means of the manifold representation techniques acquired through the course. Analysis of the behavior of the basic algorithms for automated reasoning.
Making judgements:
Being able to evaluate the quality of a representation model for a problem and the results of the application of the reasoning algorithms when run on it.
Communication:
The oral communication skills are stimulated through the interaction during class, while the writing skills will be developed thorugh the analysis of exercises and answers to open questions, that are included in the final test.
Lifelong learning skills:
In addition to the learning capabilities arising from the study of the theoretical models presented in the course, the problem solving capabilities of the student will be improved through the exercises where the acquired knowledge is applied.
