Matching system knowledge and user expectations in situation-aware systems

Abstract

Demand-orientation is of crucial importance in mobile and pervasive information services in right place. Increasing attention has been devoted to the notion of personalized services that take the situation of the user into account. The trade-off is to ensure appropriate information supply while preventing information overload. Comparing situations predicted by the system with the expectations of a user yields information about "so-far unexpected" changes the user should be informed about. This paper describes an approach to support situation-awareness and introduces an underlying model that additionally handles information from various sources. Our approach is illustrated in a driving assistance application

Swarm-based semantic fuzzy reasoning for situation awareness computing

“…Such models focus on default reasoning that results in a crisp subsumption of unclassified situations. Furthermore, the authors in [13] modeled the user context as situations. They proposed a method to retrieve such situational knowledge by applying a logical matching method against system and user expectations related to current/future situations.…”

Situation Awareness: Dealing with Vague Context

“…Such models focus on default reasoning that results in a crisp subsumption of unclassified situations. Furthermore, the authors in [26] modeled the user context as situations. They proposed a method to retrieve such situational knowledge by applying a dynamically logical matching method against system and user expectations related to current/future situations.…”