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The paper shows how semantic grid learning services will support a user-centered, personalized, contextualized and experiential based approach for ubiquitous learning. In order to allow personalized learning processes, we need to study and define methodologies for representing, through adequate knowledge structures, such as ontologies, both the area (the learning context) and the learner. In this paper we will focus on the role of personalized ontologies for a new generation of intelligent services, and more specifically, about their role for Grid Learning services in ELeGI.

This paper introduces HEMIS, a home energy management intelligent system based on a hybrid cognitivereactive multi-agent system. Thanks to smart metering, HEMIS provides real time and easy access to multiple physical properties including energy consumption, temperature, air qualityThis collected data is used by the system in order to activate predefined scenarios enabling home automation and energy saving. The system acts on the environment via actuators such as heater controllers, roller shutters on windows or doors, alarms The HEMIS consists of two layers: cognitive multiagent systems where concepts which are easily understood and used by the users, are represented by cognitive agents and a reactive layer closer to the physical world. Our system is accessible anywhere thanks to the HTML5 web application.