Representación de conocimiento Código:  M0.501    :  5
Consulta de los datos generales   Descripción   Objetivos y resultados de aprendizaje   Contenidos   Consulta de los materiales de los que dispone la asignatura  
Este es el plan docente de la asignatura. Os servirá para planificar la matrícula (consultad si la asignatura se ofrece este semestre en el espacio del Campus Más UOC / La Universidad / Planes de estudios). Una vez empiece la docencia, tenéis que consultarlo en el aula. (El plan docente puede estar sujeto a cambios).

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.


Paradigms, techniques and methodologies to represent formally and explicitly specialized knowledge domains are presented. For instance, this field contains the representation of knowledge with first-order logic, with description logics and ontologies, as well as formal representation methods and inference for knowledge related to vagueness, uncertainty, changing and planning.


The course pretends to qualify students to know the mathematica, logical and computational basis of those  broad area related to artificial intelligence and logic.


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.





  1. Introduction to Knowledge Representation  
  2. Representing Knowledge and Reasoning with First-Order Logic
  3. Representing Object-Oriented Knowledge
  4. Default Representation and Reasoning
  5. Vagueness and Uncertainty Representaion
  6. Representing Changing and Planning


Knowledge Representation Web
Lógica de predicados PDF
Lógica de enunciados PDF