mOeX aims to understand the principles guiding knowledge evolution in social agents. It considers agents behaving according to their knowledge and beliefs.
In its whole generality, this applies to human beings, possibly animals, as well as software agents. But, we study them chiefly in a well-controlled computer science context using formal knowledge representations.
There are two benefits in doing this:
This is useful, because our societies are increasingly producing knowledge-based systems which have to interact with their environments including other agents and human beings. If such systems are not able to evolve their knowledge through these interactions, they will be inadequate and may cause accidents.
This applies to social robots designed to help elderly people or smart cities monitoring the social behaviour to facilitate it.
Although we do not develop applications ourselves, we are open to collaborations.
We adopt an evolutionary perspective to this problem. Our work is based on cultural evolution which applies evolution theory to culture. It has proven useful for explaining long term social phenomena and is wide spread in social sciences and the humanities, from anthropology to social psychology.
We apply it to knowledge and beliefs considered as culture, in a computational context. More specifically, we aim at assessing the global properties of knowledge evolved through local adaptation. (This local/global opposition is understood with respect to society —individual/population—, space —close/far— and time —short-term/long-term.)
We assume that cultural evolution can be seen as a computational phenomenon, in the sense that cultural evolution applies to computational entities (this is artificial cultural evolution) and that a computational approach can help understanding cultural evolution (this is agent-based simulation of cultural evolution).
As a third line of work, we would like to improve connections with social sciences and the humanities.
More precisely, knowledge is subject to internal constraints, imposed by logical coherence, and external constraints, imposed by interaction with the environment and others.
We study populations of agents under these constraints. Interactions are carried out through precisely specified modalities (which may involve direct knowledge exchange, talking, acting together or in presence). After interacting, when they discover that their knowledge is not adequate, agents adapt their knowledge to the situation.
The repetition of such adaptations constitute the evolution of the knowledge of a population of agents to their environment.
We have performed:
Like any good research work, as it answers some questions, it brings new ones, eventually more fundamental.
Interesting questions that we would like to consider in depth now are:
There are two long-term threads of questions which are related to the dynamics of the context in which agents evolve. This occurs when agent populations:
These are the main topics. But we also consider pluripotent agents, diversity and resilience, creativity and self-motivation, value-based evolution operators, knowledge propagation and network structure as well as links with replicator-interactor.
Not in itself.
But as any kind of knowledge it may be used for good or bad. It may be used to understand how populations may better align their knowledge to less conflicting relations. It may also be used to manipulate them in order to reach a specific kind of knowledge.
More down to earth, it may be used to design robots to help elderly people in an acceptable way. Doing so, this may encourage our society to leave elderly people in the care of robots...
We develop our work with symbolic techniques which have the advantage of being more directly understandable than neuro-statistic approaches. However, the raised problems occur for such systems and may be handled along similar principles. Hence, exploring the interwinning of symbolic constraints and subsymbolic learning is a natural extension.