Artificial Intelligence (AI) refers to the design and programming of machines capable of performing tasks that require intelligence. AI research is currently focused on specific rather than general intelligence, with important advances already being made in areas such as natural language processing, artificial vision, robotics, etc. However, the ultimate objective from a scientific basic and applied research point of view remains the creation of a general AI ecosystem of intelligent, multitasking technologies.

RESEARCH AREAS AND PRIORITIES:

Machine learning: Machine learning is the process by which computers learn how to perform tasks from the empirical data provided by the programmer. The aim of this discipline is to develop generic models capable of adapting independently to specific tasks. Each model has a set of adaptable parameters that control its behaviour and allow it to be configured to perform tasks such as prediction, classification, ranking…  The ideal values for this parameters are obtained through a training process on a data set representing the task to be learnt.

      Research areas:

• • Anomaly detection methods
  • Real-time and continuous learning
  • Evolutionary computation
  • Feature engineering
  • Learning in IoT enviroments
  • Deep learning and new neural network architectures
  • Machine learning with quantum computing
  • Feature selection in low-precision and low-capacity devices
  • Complex biological systems analysis using machine learning and data integration techniques
  • Deterministic symbolic regression​

Artificial vision: Artificial vision is concerned with the development of automatic and semi-automatic methods of processing visual data obtained from static (image) and dynamic (video) sensors for the extraction of higher-level data and scene interpretation.

      Research areas:

• • Deep learning using artificial neural network models and architectures capable of learning in supervised and semi-supervised contexts
  • Medical image analysis using pattern recognition techniques
  • Dynamic scene analysis for interpretation of objects and their actions

Natural language processing: Natural language processing is the technology used to develop computational models that enable computers to understand and generate text in a particular language. Its applications include machine translation of texts, location of information online using search engines, analysis of user opinions, and automation of human tasks by virtual assistants.

      Research areas:

• • Syntactic analysis for large-scale processing
  • Advanced Question Answering techniques
  • Sentiment analysis in social media
  • Multilingual word embeddings
  • Language learning tools
  • Electronic dictionary of Spanish collocations
  • NLP techniques and resources for Galician, Portuguese and Uzbek

Intelligent autonomous robotics: Intelligent robotics is the development of real-world systems capable of responding autonomously to changing or unforeseen conditions, without the input of a programmer.

      Research areas:

• • Evolutionary robotics
  • Cognitive robotics
  • Multi-robot systems
  • Creation of robotic structures and systems

​Knowledge representation and reasoning: Knowledge representation and reasoning is the study of how to express the knowledge of an intelligent agent in different types of formal language that a computer can use to solve complex problems, reach conclusions, and answer questions about different application domains.

      Research areas:

• • Theoretical foundations of Answer Set Programming (ASP) (Equilibrium Logic)
  • Functional Logic Programming and Constraint Programming
  • Causal Explanation
  • Epistemic ASP
  • Qualitative Spatial Reasoning
  • ASP applications (medicine, forensic computing, musical composition)