In chapter 7, Mephu-Nguifo, Dillenbourg and Baker address this issue at the computational level, by comparing the operators used to model the co-construction of knowledge through dialogue and those used in machine-learning research to model individual learning.. Both sets of operators are rather similar at the knowledge level: for instance 'generalisation' can describe both the relation between two knowledge states during learning or the relationship between the semantic contents of two utterances in dialogue. However, this similarity does not extend to the strategy level: for instance, a dialogue strategy operator may be something like 'lying to check one's partner's agreement', while a learning operators would be 'focus on near-miss counter-examples'.

This paper addresses the problem of understanding the mechanisms by which learning takes place as a result of collaboration between agents. We compare dialogue operators and machine learning operators with a view to understanding how the knowledge that is co-constructed in dialogue can be learned in an individual agent. Machine Learning operators make knowledge changes in a knowledge space; dialogue operators are used to represent the way in which knowledge can 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 sets 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.