Natural contextual reasoning for end users

The realization and deployment of the Internet of Things require providing to non-programmers some level of programmatic control for tailoring system behaviour to their context and needs. We introduce a simple context-range semantics (CRS) and a context-range editor (CoRE) that support end users formulate and understand logical expressions regarding context. The editor builds on two key ideas (a) contextual information is used to evaluate and minimize logical expressions; (b) logical expressions are presented in a disjunctive normal form (DNF) thus applying a principle established in mental model theory. User tests reveal situations in which the theory regarding the intuitiveness of the DNF needs to be extended with a new element: Logical terms are easier to comprehend and formulate when grouped according to their semantic affinity. We report two experiments that demonstrate the intuitiveness of this approach and how it improves performance of non-programmers in specifying context sensitive system behaviour