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. We deal with software agents and formal knowledge representation. 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 global properties of local 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.
In addition, we still investigate rdf data interlinking with link keys, a way to link entities in different data sets.