A fuzzy based architecture for learning relevant embedded agents associations in ambient intelligent environments

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant associations between services provided by devices and embedded agents residing in Ambient Intelligent Environments (AIEs). The embedded agents perform two processes where the first process monitors the inhabitants of the AIE and learns their behaviors in an online, non-intrusive and life-long fashion. The second process then evaluates the relevance and significance of the associations to various services and eliminates the redundant associations in order to minimize the agent computational latency within the AIE. We will present real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture.