The main objective of the course is to provide the students with basic understanding of (logic-based) knowledge representation and reasoning. With this understanding, the students will be able to handle the main tools and dive deeper into the theoretical results of the area.
The expected learning results are: - Knowledge about the main knowledge representation languages and their limitations - Knowledge about the usual reasoning tasks and their complexity - Be able to derive logical consequences from explicit knowledge - Be able to analyse logical formulas and their intended meaning - Apply logic-based knowledge representation to advanced AI applications
Course Prerequisites
Basic notions of algebra, logic, and set theory
Teaching Methods
The course is based on frontal lectures and exercise sessions. For the lessons, slides and text handouts will be provided. The exercises will be given in advance and solved at the classroom.
Assessment Methods
The course will be evaluated through a written exam based on several kinds of questions. In particular, it will contain multiple-answer, open-answer, and exercise solving questions. The exercises solved during the lecture and additional examples provided during the lecture will serve as practice for the examination.
