• Descripción

The subject deals with the knowledge representation and reasoning for intelligence systems. It aims at representing knowledge with formal logic languages in order to allow inferencing from those knowledge, creating new elements of knowledge. The course pretends to qualify students to know the logical and computational basis of those  broad area related to artificial intelligence and logic.

• Objetivos y competencias

The main two goals are:

• To introduce students in the research field of Knowledge Representation and Reasoning (KRR), offering both theoretical and practical learning experiences. In particular, during the course students will have to understand the basic KRR systems and to read scientific papers in a critical way.

• To develop students' formal modelling skills.  In particular, throughout the course students will have to develop formal modelling and reasoning skills.

Course objectives are derived from the main goals and designed to be assessable:

• To understand the main paradigms for knowledge representation, its concepts and methods of inference
• To know propositional logic and first order logic as a foundation for knowledge representation.
• To know the descriptive logic and its application to the inference and the semantic web ontologies.
• To comprehend modelling the temporal aspects of intelligent systems.
• To know how to model uncertainty and partial information and learn about their methods of inference.
• To understand probabilistic methods of representation and reasoning and their application to real problems.
• To apply scientific thinking to the analysis of complex systems and processes.
• To read scientific papers on the KRR rearch field.

• Contenidos

Introduction to Knowledge Representation research field.  First order logic. Formal ontologies and the Semantic Web. Descriptive logics. Temporal reasoning. Modal logics of knowledge and belief . Non-monotonic logics. Probabilistic reasoning.

• Consulta de los materiales de los que dispone la asignatura