Engineering in Computer Science and Artificial Intelligence

General objectives. The course aims to introduce the fundamentals of Artificial Intelligence, with a particular emphasis on automated reasoning and sequential decision making. Students will become familiar with the main formalisms and approaches for knowledge representation and reasoning, in both static and dynamic contexts. They will also learn the basics of decision making approaches for deterministic, non-deterministic, adversarial, and stochastic domains. Specific objectives. Knowledge and understanding: Students will be introduced to the basics of Knowledge Representation for static and dynamic systems through formal approaches: propositional and first-order logic, situation calculus, MDPs. The fundamental logical tasks (evaluation, satisfiability, validity, logical implication) will be studied and basic solution techniques (DPLL, tableau method) will be learnt. The goal is to understand the importance of the formal model and of domain-independent approaches as fundamental tools to automatically solve problems. Students will learn how to model a Planning domain through the PDDL language and how to solve planning problems in deterministic, non-deterministic, adversarial, and stochastic scenarios. Essential forward state-space search techniques will be introduced: uninformed search, heuristic search, best-first search, A* search, AND-OR search. For stochastic scenarios, Policy Evaluation and Policy Iteration will be learnt. Applying knowledge and understanding: Students will learn how to abstract and model real-world scenarios as static or dynamic domains in a rigorous way, as well as to identify and formalize real-world problems. They will also be able to apply the techniques acquired during the course to solve the modelled problems. By understanding how to formally model and solve problems, students will become able to design and implement simple reasoning systems for a variety of real-world scenarios and related problems. Making judgements: Students will be able to evaluate the appropriateness and quality of a representation formalism with respect to various classes of problems and to select the most suitable solution technique. Communication: The course will provide students with the basic notions and vocabulary to effectively interact with their pairs and experts in the area. Oral communication skills are stimulated through the interaction during class, while writing skills are developed through the analysis of exercises and answers to the open questions included in the final test. Lifelong learning skills: The course will provide students with the essential tools needed to access the specialised literature. In this way, they can autonomously strengthen and broaden their competencies. In addition to such learning capabilities, students will also acquire advanced modelling and general problem solving skills.