Agent modelling for CSCL environments using answer sets programming

In this paper, we present the computational model of an agent for CSCL environments, based on the answer set programming (ASP) formalism. The complete model is formally presented in the declarative language of DLV, a system for implementing ASP models. We propose a representation schema for the agent´s beliefs about the learner and the domain, together with the corresponding inference system with the appropriate rules to derive new beliefs about the capabilities of the learner, and its use in order to support effective collaboration and maintain learning possibilities for the group members. The model proposed provides a representation of the structural knowledge frontier and the social knowledge frontier of the learner, which are the components for the definition of the learner´s zone of proximal development (ZPD). Based on the ZPD of its learner the agent can propose her an intelligent learning task. The model includes a concept of group supportive task that allows the agents to maintain the ZPD for the learners in the group, as presented in the results.