Effective Appliance Selection by Complementary Context Feeding in Smart Home System

Smart Home System (SHS) is one of popular applications in ubiquitous computing, which provides convenient services for a user with userfriendly intelligent system interfaces. Among them, voice recognition is a popular interface. However, voice command statements given by users are often too unclear and incomplete for the devices in SHS to understand the original user intention. So, the devices become complicated and have no idea about whether to work or not. Therefore, we should make sure the proximate selection for the devices which will be eventually targeted and operated following user intention. In this paper, we propose an effective method to make a decision in electing a promising target device among candidates by taking advantage of complementary context feeding around user environment in SHS even with initial incomplete interface information. The proposed method is based on Bayes theorem using the way of empirical statistic inference.