Opinion Nets for Reasoning with Uncertain Context Information

Context-aware systems must be able to deal with uncertain context information. We propose a generic context architecture and representation that incorporates the uncertainty of context elements in terms of upper and lower bounds of probabilities. It is shown how opinion nets can be used to reason with these upper and lower bound probabilities. In this way it is possible to combine ambiguous or conflicting context information that comes from different sources. Moreover, information coming from different sources can be combined with experience learned from the past in a clean way.