Representação de Conhecimento e Sistemas de Raciocínio

Knowledge :

• Main languages ​​for the representation of ontologies.
• Reasoning algorithms for these languages, and their usefulness.
• Ontology-based data acess (OBDA).
• Interplay between the expressiveness of representation languages ​​and the computational complexity of reasoning.
• Limiations of these ontology languages for representing common sense knowledge.
• Answer Set Programming as a language ​​for the representation of common sense knowledge in Artificial Intelligence and for solving large combinatorical problems.
• Reasoning algorithms on this language, and their usefulness.
• Efficient Modelling for Answer Set Programming.
• Learning logic programs (for Answer Set Programming).

Know-how :

• Use knowledge representation languages for modelling Databases, and simple application domains in Artificial Intelligence.
• Apply reasoning mechanisms for ​​knowledge representation languages to test for the correctness of Database models and to formulate more expressive Database queries.
• Apply reasoning mechanisms for ​​knowledge representation languages ​​to derive consequences in general domain areas (in Artificial Intelligence).
• Relate these knowledge representation languages ​​with data integration problems in Databases.
• Use these knowledge representation languages ​​for problem modelling and solving, including problems of satisfaction and scheduling.
• Use systems that implement reasoning mechanisms for ​​knowledge representation languages efficiently, and integrate with other systems for building applications.

Soft-Skills :

• Apply formal theoretical knowledge in real-world applications.
• Autonomous research for problem solving.
• Become aware of design trade-offs.