Autonomous Learning of User's Preferences Improved through User Feedback

Ambient Intelligent (AmI) environments are supposed to act proactively anticipating the user’s needs and preferences, therefore the capability of an AmI system to learn those elements out of daily life behaviour of those using the environment is very valuable. In this paper we present a system that discovers patterns related to user’s actions and improves them through user feedback. The core of this system is an algorithm which taking as starting point information collected by sensor discovers these patterns. Coupled with the algorithm, a language to represent those patterns has been developed. This system allows the user experiencing the AmI environment to verbally interact with the system and give his/her feedback about patterns that have been discovered. The speech based interaction provides a natural communication for the user and the simple protocol established makes the system available to users without sophisticated training.