Intelligent Context with Decision Support under Uncertainty

Context is inherently complex requiring intelligent context processing to address the broad and diverse range of available data that when processed can be viewed as contextual information useful in intelligent context-aware systems in a broad range of domains and systems. The function of context-aware systems is to target service provision based on an entities context, an entity has been defined as "a person, place, or physical or computational object". Given the diverse range of available data and entities a primary requirement for an intelligent context-aware system is decision support under uncertainty. This paper provides an overview of context and intelligent context processing. The novel \´extended\´ context matching algorithm is presented with an overview of fuzzy systems design as it relates to context processing and matching. The design of the membership function is discussed and it is shown that context matching using a membership function based on semantic representation provides a basis upon which the granularity of the context matching result can be improved. Anomalies where the context matching result lies close to a decision boundary are discussed with context processing under uncertainty. The paper concludes with a discussion conclusions, and open research questions.