"Inferring Cause/Effect Relationships in Multi-sensor Ambient Intelligence Systems"

In this work a learning technique to provide an Ambient Intelligence (smart space) system with the capacity of predicting variation events in its own internal state is presented. The system and the interacting users are modeled through the instantaneous state vectors obtained as output of two trained Self Organizing Map-based classifiers. The information processed by the system is collected by two sensors sets monitoring several internal and external system variables. Starting from the hypothesis that the user actions have a direct influence on internal system state variables (e.g. work load on personal computers computation or storage devices in a University laboratory, in our current test implementation) we developed a statistical voting algorithm for inferring cause/effect relationships in these instantaneous variations. Logical connections are obtained in unsupervised mode with no a priori information and leads to the definition of a knowledge base the system can exploit to predict its own near future internal state variations, given the observation of the lab users