A probabilistic reasoning framework for smart homes

Abstract

Inference and reasoning in modern AmI (Ambient Intelligence) middlewares is still a complex task. Currently no common patterns for building smart applications can be identified. This paper presents an ongoing effort to build a generic probabilistic reasoning framework for the networked homes. The framework can be utilized for designing smart agents in a systematic and unified way. The developed modeling and reasoning algorithms make an extensive use of the information about the user and the way he/she interacts with the system. To achieve this, several levels of knowledge representation are combined. Each level enriches the domain knowledge in a way that a consistent, user-adaptable probabilistic knowledge base is constructed. The facts in the knowledge base can be used to encode the logic for a specific application scenario.