CID (Knowledge, Uncertainty, Data)
CID’s research area is Artificial Intelligence. Research concerns statistical learning, managing uncertainty, and knowledge engineering with its applications in knowledge capitalization, recommender systems, and the scripting of virtual environments.
Research topics
Knowledge and Data Processing
This research topic covers upstream aspects of the processing of knowledge and data. We develop formalisms, models and algorithms for manipulating symbolic knowledge and numerical data, using different types of logic (propositional, descriptive, modal, etc.) and statistics.
- Knowledge modelling for capitalizing, explaining and reasoning
- Managing uncertainty in the processing of weak data
- Prudent inference
- Interactive learning
- Learning structured data
Personalized Adaptive Systems
This research topic seeks to build a bridge between, on the one hand, work on symbolic knowledge representation and human-system interaction, and on the other, numerical methods for dealing with uncertainty and statistical learning. The objective is to design systems capable of adapting automatically and dynamically both to the humans that use them and to the environment they operate in.
The methods that we develop are tested in collaborative environments for capitalizing knowledge, recommending tourist and cultural circuits, and scripting virtual environments for human learning.
- Dynamic learning of user profiles and preferences.
- Adapting system behaviour.