Merging context perspectives: an approach to adaptive agent reasoning in pervasive computing systems

Abstract

In open, heterogeneous, context-aware pervasive computing systems, suitable context models and reasoning approaches are necessary to enable collaboration and distributed reasoning among agents. This paper proposes, develops and demonstrates a novel approach to perform distributed reasoning by merging and partitioning context models that represent different perspectives over the object of reasoning. We show how merging different points of view contributes to an enhanced outcome in reasoning about context