The main aim of the Knowledge Engineering and Machine Learning Group (KEMLg) is the analysis, design, implementation and application of several Artificial Intelligence techniques and methodologies to support the operation or behaviour analysis of real-world complex systems or domains. The research is focused on the analysis, design, management or supervision of these domains, such as in healthcare, in environmental processes and systems, in social and internet-based systems and in the industrial and enterprise sector. Main research efforts are focused in the following areas:

Active in the Artificial Intelligence (AI) field since 1986, the group has made contributions mainly in intelligent agents development, understanding of coalition setting dynamics, social structure dynamics analysis, formal model construction for norms and conventions for e-commerce, information flow process design, temporal episode-based reasoning, experience-based argumentation techniques, hybrid methods in Statistics and Artificial Intelligence, belief or bayesian networks, case-based reasoning, knowledge-based systems, supervised and unsupervised machine learning techniques, knowledge model identification and knowledge model building, knowledge representation, ontologies, social networks, semantic Web, Web services, and directory service study.

The group's activity is supported via both national and European-funded projects, and contracts with companies and organizations. Furthermore, some group members are involved in start-ups in the ICT Environmental sectors.