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Many context-aware services make the assumption that the context they use is completely accurate. However, in reality, both sensed and interpreted context is often ambiguous. A challenge facing the development of realistic and deployable context-aware services, therefore, is the ability to handle ambiguous context. In this paper, we describe an architecture that supports the building of context-aware services that assume context is ambiguous and allows for mediation of ambiguity by mobile users in aware environments. We illustrate the use of our architecture and evaluate it through three example context-aware services, a word predictor system, an In/Out Board, and a reminder tool.

A significant problem encountered when building Augmented Reality (AR) systems is that all spatial knowledge about the world has uncertainty associated with it. This uncertainty manifests itself as registration errors between the graphics and the physical world, and ambiguity in user interaction. In this paper, we show how estimates of the registration error can be leveraged to support predictable selection in the presence of uncertain 3D knowledge. These ideas are demonstrated in osgAR, an extension to OpenSceneGraph with explicit support for uncertainty in the 3D transformations. The osgAR runtime propagates this uncertainty throughout the scene graph to compute robust estimates of the probable location of all entities in the system from the user's viewpoint, in real-time. We discuss the implementation of selection in osgAR, and the issues that must be addressed when creating interaction techniques in such a system.