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Research Groups | Artificial Intelligence

The objective of the Group of Artificial Intelligence (AI) is the development of theoretical and applied research on methods and techniques for AI used in several parts of this broad area.
Research results are used in the development of systems aimed at the following application areas: education, petroleum industry, medicine, Internet, traffic, bioinformatics, natural language processing, simulation of complex environments and social simulation, image processing, data mining; corporate memories; systems to support decision-making; fraud detection; speech recognition, computer education; intelligent games and robotics.

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Members of the Research Group

Research Themes

  • Simulation of Complex environments: traffic simulations, traffic control, game theory, models and tools for Social Simulation.
  • Image Processing: automatic image analysis for pattern recognition, novelty detection and information extraction.
  • Knowledge Discovery and Management and Data Mining: information extraction from databases including distributed databases.
  • Fraud Detection: fraud detection systems using AI techniques.
  • Natural Language Processing and Speech Recognition: development of techniques and systems for text and speech-based computer interactions. Tools and systems for supporting the development of electronic dictionaries and written text processing (newspapers, books, blogs, ..).
  • Computing in Education: development of mobile (e.g. cell phones, palmtops, etc.) and distance education systems. Intelligent tutoring systems and Web-based student profile tracking systems.
  • Intelligent Games: development of techniques and systems for intelligent games in remote and mobile devices (e.g. cell phones, palmtops, etc.).
  • Autonomous and Intelligent Robotics: robotic systems that use Artificial Intelligence techniques for autonomous processing.
  • Bioinformatics: investigation and implementation of techniques and systems for supporting genome sequencing.
  • Knowledge Engineering and Ontologies: knowledge acquisition and conceptual modelling for building knowledge systems. Reasoning and modelling for visual knowledge.


Recent Research Projects

  • Cognitive Computational Models of Natural Languages for Assessing Language Competency Dr. Aline Villavicencio, CNPq / MIT (US)
  • INB: a probabilistic approach to autonomous learning. Dr. Paulo Engel, CNPq
  • Geochemistry modelling. Dr Mara Abel, Petrobras
  • LabTrans – Computational laboratory for simulation and testing of policies for urban mobility. Dr. Ana Bazzan, CNPq
  • Machine learning and multiobjective optimisation: application in structural bioinformatics. Dr. Luis Lamb, CNPq
  • Non classical logics in computer scence. Dr. Luis Lamb, CNPq
  • OBAA-MILOS: multiagent infrastructure for teaching and learning. Dr. Rosa Maria Vicari, FINEP
  • Ontologies for stratigraphic description in petroleum exploration. Dr Mara Abel, ENDEEPER
  • Saving travel time through inter-vehicle communication. Dr. Ana Bazzan, CNPq / DLR (Germany)
  • Standard for Ontologies for Robotics and Automation. Dr. Edson Prestes, IEEE

Recent Research Results

  • Proposal for a Brazilian metadata standard for Learning Objects that is compatible with the Digital Television, Internet and Mobile Devices.
  • Mechanism for search and retrievas of educational material by means of their metadata.
  • Tool for supporting the decision-making process in urban mobility and smart transportation projects.
  • Full domain ontology for petroleum reservoir petrography that has resulted in the commercial product PETROLEDGE System.
  • Full domain visual ontology for Stratigraphy, producing standard nomenclature for drill core and field outcrop description.
  • Collaborative portal for domain ontology construction, applied to geology building of domain ontology (available at http://obaita.inf.ufrgs.br)