A comparison of learning and dialogue operators for computational models.
Engelbert Mephu Nguifo, Pierre Dillenbourg, Michael J. Baker · 1999
This paper compares dialogue operators and machine learning operators from the point of view of understanding the mechanisms by which learning takes place as a result of collaboration between agents. Machine Learning operators are operators that make knowledge changes in the knowledge space. Dialogue operators are operators used in collaborative learning dialogues as transformation functions that allow knowledge to be co-constructed in dialogue. We describe the degree of overlap between both sets of operators, by applying learning operators to an example of dialogue. We review several differences between these two set of operators: the number of agents, the coverage of strategical aspects and the distance between what one says or hears and what one knows. We discuss the interest of fusing dialogue and learning operators in the case of person-machine cooperative learning and multi-agent learning systems.