MOEX Research team
Human beings are apparently able to communicate knowledge. However, it is impossible for us to know if we share the same representation of knowledge.
mOeX addresses the evolution of knowledge representations in individuals and populations. The ambition of the mOeX project is to answer, in particular, the following questions:
- How do agent populations adapt their knowledge representation to their environment and to other populations?
- How must this knowledge evolve when the environment changes and new populations are encountered?
- How can agents preserve knowledge diversity and is this diversity beneficial?
We study them chiefly in a well-controlled computer science context.
For that purpose, we combine knowledge representation and cultural evolution methods. The former provides formal models of knowledge; the latter provides a well-defined framework for studying situated evolution.
We consider knowledge as a culture and study the properties of adaptation operators applied by populations of agents by jointly:
- experimentally testing the properties of adaptation operators in various situations using experimental cultural evolution, and
- theoretically determining such properties by modelling how operators shape knowledge representation.
We aim at acquiring a precise understanding of knowledge evolution through the consideration of a wide range of situations, representations and adaptation operators.
Our work is currently developed along two axis:
- Cultural kowledge evolution, and
- Study of link keys.
Research teams of the same theme :
- CEDAR - Rich Data Exploration at Cloud Scale
- GRAPHIK - GRAPHs for Inferences and Knowledge representation
- LACODAM - Large scale Collaborative Data Mining
- LINKS - Linking Dynamic Data
- MAGNET - Machine Learning in Information Networks
- ORPAILLEUR - Knowledge representation, reasonning
- PETRUS - PErsonal & TRUSted cloud
- TYREX - Types and Reasoning for the Web
- VALDA - Value from Data
- WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
- ZENITH - Scientific Data Management
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