Mining the Web to Advise Students using the Learning Multi-agent System MASACAD

M.S. Hamdi (UAE)


Information Customization, Multi-Agents, e-Learning, Machine Learning, Neural Networks, User Modeling, Web Mining, Expert Systems.


The recent popularity of the World Wide Web (Web) has provided a tremendous opportunity to expedite the dispersement of various information creation/diffusion infrastructures. The mass of content available on the Web raises important questions over its effective use. To cope with such environments, the promise of information customization systems is becoming highly attractive. In this paper we present MASACAD, a multi-agent system that learns to advise students by mining the Web and discuss important problems in relationship to information customization systems and smooth the way for possible solutions. The main idea is to approach information customization using a multi-agent paradigm in combination with a number of aspects from the domains of machine learning, user modeling, and Web mining.

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