An adaptive scenario-based reasoning system across smart houses

The intelligent smart home provides homeowners with various services that incorporate knowledge reasoning. However, programmers must consider such constraints as scenarios in different houses, scenarios of different users, and even different resources. That is infrastructure deployment and scenario designing are time consuming. Actually, designing a home based on the behaviors of family members is more reasonable compare with having users adapt to the functionalities of a home. Therefore, developing an efficient reasoning system for smart homes has gained considerable attention. This work presents a smart home reasoning system called the adaptive scenario-based reasoning (ASBR) system. This system learns from user preferences using adaptive history scenarios and it is a convenient method for rebuilding reasoned knowledge compare with other smart homes. Ontology based contextual information is able to be extracted from a smart home and considered as a set of scenarios. Additionally, the system derives personalized habits and store in web ontology language (OWL) files. This work then presents a novel scenario reconstruction method under computational and resource restriction. Finally, an experiment is designed for a realistic smart home and some scenarios are used to discuss the results.