Preference Learning on an OSGi Based Home Gateway

The goal of ubiquitous computing is to create intelligent environment. To make the environment adapt rationally according to the desire of users, the system should be able to guess users' interest, by learning users' preferences. Users' preferences are sometimes conflicting and needs to be resolved. When many users are involved in a ubiquitous environment, the decisions of one user can be affected by the desires of others. This makes learning and prediction of user preferences difficult. In this paper we prove that learning and prediction of user preference is NP-hard. So, we propose Bayesian RN-metanetwork, a multilevel Bayesian network to model user preference and priority. This is a semi optimal online learning approach. By using game theory we prove that the method we use will certainly converge after a while. We also provide implementation details of the metanetwork on an OSGi based home gateway.