Model-Based Default Refinement of Partial Information within an Ambient Agent

Ambient agents react on humans on the basis of partial information obtained by sensoring. Appropriate types of reactions depend on in how far an ambient agent is able to interpret the available information (which is often incomplete, and hence multi-interpretable) in order to create a more complete internal image of the environment, including humans. This interpretation process, which often has multiple possible outcomes, can make use of an explicitly represented model of causal and dynamic relations. Given such a model representation, the agent needs a reasoning method to interpret the partial information available by sensoring, by generating one or more possible interpretations. This paper presents a generic model-based default reasoning method that can be exploited to this end. The method allows the use of software tools to determine the different default extensions that form the possible interpretations.