A MULTIAGENT SYSTEM FOR PERSONALIZED RECOMMENDATION OF LEARNING OBJECTS
Ana Casali, Valeria Gerling, Claudia Deco, Cristina Bender · 2010
paper a multiagent Educational Recommender System is presented. The purpose of this system is to select the best learning objects from a federation of repositories according to characteristics and preferences of each user. This system has a multiagent architecture and one of its main agents, the Personalized Search Agent (PS-Agent), is modeled as a graded BDI agent. The graded BDI agent model allows us to specify an agent architecture able to deal with graded mental attitudes. We focus on the implementation aspects of the recommender system and especially on the PS-Agent development. Also a case study, which shows promising results in learning objects ranking, is presented.