Attending to these aspects will not only be crucial for agents participating in a collaboration, but for other types of agents. We propose a new type of agent, an observer, that calculates statistics regarding collaboration. Information sources appear and disappear, making information filtering a crucial capability of any Internet-based information gathering system. Many of these systems use machine learning techniques to non-intrusively infer the user preferences. They can be teachers who analyze interactions to detect when they should intervene, judges or referees who intervene only in case of conflict, etc.
Students build a diagrammatic representation with arguments.
They can invoke an advisor who points to specific parts of the diagram and proposes ways of extending or revising it. Some argument structures are expected to be more effective in stimulating critical discussions among students. The core task of critics is the recognition and communication of deficiencies in a product to the user. We propose a fourth category of agents, observers, i.e. agents who collect information regarding users interactions, aggregate observations into high level indicators and display these indicators to the human coach or to the users themselves. Observers would support this monitoring process both for human and artificial coaches.