m O e X

Research: objectives

mOeX addresses the seamless evolution of knowledge representations in individuals and populations. The question at the core of our proposal is to understand how to make knowledge representation continuously evolve in presence of environment changes and new knowledge sources. Currently, no satisfactory solution to this problem exists.

To tackle this problem, we start from two specific hypotheses:

Knowledge is shaped by both experience and communication,
which act as selective pressure.

More precisely, knowledge is subject to internal constraints, imposed by logical coherence, and external constraints, imposed by the environment and communication with others.

Abstract context.

Based on such hypotheses, we study populations of agents sharing knowledge through interaction. The interactions may be carried out through precisely specified modalities (which may involve direct knowledge exchange, talking, acting together or in presence). After interacting, when they discover that constraints have changed, agents will not relearn knowledge from scratch. Instead, adaptation operators, taking into account the current knowledge and other constraints, will adapt it to the new constraints. We study how knowledge evolve when these populations:

experience changes in the environment in which they operate, or
encounter other populations with which they have to communicate.

Our goal is to establish the properties satisfied by the resulting knowledge at the scale of a population of agents. Hence, mOeX aims at establishing the global properties achieved by local operators.

Pressure on knowledge and evolution in the face of disruptive events.

The highly difficult problem is not to have procedures allowing such agents to converge towards a common state of knowledge, but to characterise this state by the properties satisfied by the resulting knowledge. Such properties may, for instance, be:

Agents converge to a common knowledge representation (a convergence property).
Agents converge towards different but compatible (logically consistent) knowledge (a logical epistemic property), or towards closer knowledge (a metric epistemic property).
That under the threat of a changing environment, agents which have operators that preserve diverse knowledge recover faster from the changes than those which have operators that converge towards a single representation (a differential property under environment change).

Moreover, this has to be guaranteed in the long term involving both environment change and population encounter. Hence, it is critical that convergence towards a particular state does not turn into a handicap when the environment changes. This means that a delicate balance has to be found between adapting optimally to the current situation and interlocutors and evolvability in the long run.

What is radically new here is that these problems are approached from the standpoint of the resulting knowledge representations. mOeX work will contribute to answer the following questions:

How can populations with different knowledge (representation) communicate?
How is their representation influenced by their environment and communication with others?
How may knowledge diversity be preserved and is it useful?

In their whole generality, such questions apply to human beings, possibly animals, as well as software agents. We propose to study them chiefly in a well-controlled computer science context.

Our ambition is to spark a new approach to knowledge evolution that we call computational cultural knowledge evolution. It designs, studies, and experiments with mechanisms for making knowledge representations serendipitously evolve through their use. This should enable developing and sharing complex knowledge in a more robust way.

Now is the right time to start such a research programme: on the one hand, developments on the semantic web provide us with proven knowledge representation formalisms and tools which have been designed for sharing knowledge; on the other hand, work on experimental cultural evolution provides a solid methodology for carrying out this type of research. This approach has not been applied yet to knowledge representation directly. Both fields are mature enough to be associated.