Towards Reasoning About Context in the Presence of Uncertainty

Towards Reasoning About Context in the Presence of Uncertainty, Dan Chalmers, Naranker Dulay, Morris Sloman. In proceedings of Workshop on Advanced Context Modelling, Reasoning And Management at UbiComp 2004 Nottingham, UK, (2004)

The authors show how the relationship between real world actors (person, device, room, …) and contextual information can be formulated when uncertainty can be defined with numerical context values.

Context is not static in de nition or state – one of the properties of context is that it describes a changing relationship between users, systems and their environment. Describing these relationships is crucial in any model of context which seeks to address scenarios beyond isolated users. A key issue is the treatment of uncertainty in the relationships – the quality of the sensed context data will vary due to noisy sensors, erroneous readings, out of date data etc.

Their view of context is a set of name and value pairs. This requires that the values are typed and the possibility that the values from sensors may not be precise.

The model of values can be arranged to return a value range for a confidence level. An advantage of this approach is that a trade-off between certainty and cost (power, network load, processing time, memory use, etc) is possible where context sensing is distributed:

Sensor error (both inherent granularity and due to false readings), out of date data and poor predictions will give rise to some uncertainty about sensed context in most cases. To some extent this may be mitigated by applying fusion to multiple readings [7], but some uncertainty will remain. If an application could describe the confidence it requires in the context data, the returned value can be a value range which the context awareness system believes includes the current context within the certainty constraint. It can be expected that a higher confidence can be given to a larger range of values, while a response with a smaller range may be given if one relaxes the need to include less likely possibilities.

They present definitions of relationships which are applicable to realistic context values, using numerical and tree based unit systems with uncertain values.