A Multiagent Model for Searching Learning Objects in Heterogeneous Set of Repositories

This paper proposes a multiagent system application model for indexing and retrieving learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered.