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Course description
  AUTOMATED REASONING

Competencies and objectives

 

Course context for academic year 2018-19

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.

 

Competencias Básicas

  • CB3 : Que los estudiantes tengan la capacidad de reunir e interpretar datos relevantes (normalmente dentro de su área de estudio) para emitir juicios que incluyan una reflexión sobre temas relevantes de índole social, científica o ética.

 

 

 

Learning outcomes (Training objectives)

No data

 

 

Specific objectives stated by the academic staff for academic year 2018-19

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.

 

 

General

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

Departments involved

  • Dept: SCIENCE OF COMPUTING AND ARTIFICIAL INTELLIGENCE
    Area: SCIENCE OF COMPUTING AND ARTIFICIAL INTELLIGENCE
    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