REDUCCIÓN DEL COSTE COMPUTACIONAL DE LA INFERENCIA DE CONTEXTO BAYESIANA MEDIANTE EL USO DE RANGOS DE VALORES DINÁMICOS

Abstract

This paper shows how to reduce evaluation time for context inference. Probabilistic Context Inference has proven to be more powerful and better adapted to the challenges of the physical reality with uncertain or missing information. As the inference complexity is very high, the complexity of the to be evaluated rule (representing a share of the real world) should be reduced as far as possible. Therefore we present an approach to select only relevant values of context types and to adapt this selection during its usage time. In an evaluation we show that with only a few evaluations of the reduced inference rules the reduction costs will have amortized and the system brings significant benefit to context aware computing.