A cognitive architecture for ambient intelligence systems

Nowadays, the use of intelligent systems in homes and workplaces is a well-established reality. Research efforts are moving towards increasingly complex Ambient Intelligence (AmI) systems that exploit a wide variety of sensors, software modules and stand-alone systems. Unfortunately, using more data often comes at a cost, both in energy and computational terms. Finding the right trade-off between energy savings, information costs and accuracy of results is a major challenge, especially when trying to integrate many heterogeneous modules. Our approach fits into this scenario by proposing an ontology-based AmI system with a cognitive architecture, able to perceive the state of the surrounding environment, to reason on the current situation and act accordingly to modify the state of the environment based on the user’s preferences.