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Socially Intelligent Agents in Educational Games 217reaction) and updates the corresponding elements in the student model for thatplayer.A Game Actions Interpreter, for instance, processes all the student’s gameactions within a specific activity, to infer information on the student’s cognitiveand meta-cognitive skills. A Meta-Cognitive Behavior Interpreter tracks allthe additional student’s actions that can indicate meta-cognitive activity, (e.g.,utterances and eye or mouse movements) and passes them to the student modelas further evidence on the student’s meta-cognitive skills. The agent’s actiongenerator then uses the student model and the expertise encoded in the agent’sknowledge base (which depend on the agent’s pedagogical role) to generateactions that help the student learn better from the current activity.The agents in the architecture include a Game Manager, the CollaborationManager and agents related to specific game activities (like Help Agent foractivity A and Peer Agent for activity K in Figure 1). The Game Managerknows about the structure of the game and guides the students through itsactivities. The Collaboration Manager is in charge of orchestrating effectivecollaborative behavior. As shown in Figure 1, its Behavior Interpreter capturesand decodes all those students’ actions that can indicate collaboration or lackthereof, along with the related emotional reactions. The actions that pertainto the Collaboration Manager include selecting an adequate collaboration roleand partners for a student within a particular activity. The pool of partners fromwhich the Collaboration Manager can select includes both the other players orthe artificial agents (e.g., the Peer Agent selected for Student N in activity K inFigure 1), to deal with situations in which no other player can currently be anadequate partner for a student, because of incompatible cognitive or emotionalstates.The artificial agents related to each game activity have expertise that allowthem to play specific roles within that activity. So, for instance, a Help Agent(like Help Agent for activity A in Figure 1) has expert knowledge on a givenactivity, on the emotional states that can influence the benefits of providing helpand on how to provide this help effectively. Peer agents, on the other hand, willhave game and domain knowledge that is incomplete in different ways, so thatthey can be selected by the Collaboration Manager to play specific collaborativeroles in the activity (e.g., that of a more or less skilled learning companion).3.2 Student ModelsThe student models in our architecture are based on the probabilistic reasoningframework of Bayesian networks [10] that allows performing reasoningunder uncertainty by relying on the sound foundations of probability theory.One of the main objections to the use of Bayesian networks is the difficultyof assigning accurate network parameters (i.e. prior and conditional proba-