Knowledge Representation (KR) is the study of how knowledge can be encoded in a machine, in such a way that the machine can use this knowledge to understand the world, solve problems, perform tasks or achieve goals. KR is one of the main areas in the field of Artificial Intelligence (AI), but it has deep roots in fields like philosophy, psychology, linguistics and mathematical logic. This course introduces the student to the main principles and methods of KR, with a special emphasis on its applicability to the area of robotics. The course comprises a mixture of top-down teaching on the basic principles, and bottom-up self-study on individual advanced issues.
The course will cover the following topics:
- The influence from cognitive sciences
- The influence from formal systems
- Ontologies and ontology management tools
- Dealing with specialized types of knowledge: spatial, temporal, causal, normative, etc.
- Dealing with uncertainty: quantitative and qualitative tools, semantics of uncertainty.
- Additionally, some specific topics will be discussed on a year-by-year basis, depending on the interests of the students and on the current advances of the field.