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Course description

Competencies and objectives


Course context for academic year 2021-22

Automated Reasoning is a subject that provides an overview of actual techniques and discussions of artificial intelligence based on human reasoning. It ranges from the classic logics to the most modern Machine Learning, Learning Theory and the PAC model.

Despite having a particular agenda, Automated Reasoning is a subject open to all topics, allowing students to develop, know and practice them from a professional point of view, with real problems. The focus of the course is entirely practical, with hands on problems for students that will take their skills to the limit, improving and expanding them.

Posed problems will use simulation environments based in computer games and nowadays Artificial Intelligence contests. In general, the subject will follow a Project Based Learning approach.



Course content (verified by ANECA in official undergraduate and Master’s degrees)

Specific Competences (Specific Technology):>>Computer Science

  • CEC4 : Capacity to understand the foundations, paradigms and techniques associated with intelligent systems and analyse, design and build computer systems, services and applications that use said techniques in all fields of application.
  • CEC5 : Capacity to acquire, obtain, formalise and represent human knowledge in computable fashion to solve problems using computer systems in any field of application, particularly those associated with aspects of computing, perception and activities in intelligent environments.
  • CEC7 : Capacity to understand and develop computational learning techniques and design and implement applications and systems that use them, including those for the automatic extraction of information and knowledge from large volumes of data.


Basic Competences

  • CB3 : Students must be able to gather and interpret relevant data (usually within their area of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues.




Learning outcomes (Training objectives)

No data



Specific objectives stated by the academic staff for academic year 2021-22

Course goals

  • Analyze AI from the point of view of Automated Reasoning.
  • Understand the importance of Automated Reasoning as an application in video games.
  • Learning how to properly design an Artificial Intelligence Engine.
  • Learn to design, implement and optimize an AI under low computational limitations.
  • Learn and implement basic techniques of Automated Reasoning like Finite State Machines, Decision Trees, Fuzzy Logic and Goal-Driven Reasoning.
  • Learn to improve Pathfinding and planning models to produce results of Automated Reasoning.
  • Know Machine Learning, the PAC model (Probably Approximately Correct), Learning Theory and Generalization.
  • Learn and implement some Machine Learning models like Linear and Logistics Regression, Neural Networks, Genetic Algorithms and Support Vector Machines.




Code: 34031
Lecturer responsible:
Credits ECTS: 6,00
Theoretical credits: 0,60
Practical credits: 1,80
Distance-base hours: 3,60

Departments involved

    Theoretical credits: 0,6
    Practical credits: 1,8
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught