Representación de conocimiento Código:  M0.501    :  5
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The subject deals with the topic of Knowledge Representation and Reasoning in intelligent systems. It aims at representing knowledge with formal logic languages in order to allow making inferences from it, creating new elements of knowledge. The course intends to qualify students to know the logical and computational basis of this broad area related to artificial intelligence and logic.

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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 content 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 paradigm of knowledge representation, its concepts and methods of inference.
  • To know propositional logic and first order logic as a foundation for knowledge representation.
  • To know description logics and its application to the inference and the semantic web.
  • To know and practice with logic programming in Prolog.
  • To know how to model belief, uncertainty and partial information.
  • To understand the application of knowledge representation and reasoning to intelligent systems and real problems.
  • To apply scientific thinking to the analysis of complex systems and processes.
  • To read scientific content on the KRR research field.

 

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Introduction to Knowledge Representation research field.  First order logic.  Description logics. Formal ontologies and the Semantic Web. Logic programming. Formal management of default reasoning and uncertainty.

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Lógica de enunciados PDF
Lógica de predicados PDF
Knowledge Representation Web
MATLAB_ENG PDF

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