Learning frequent behaviours of the users in Intelligent Environments

摘要

Intelligent Environments (IEs) are expected to support people in their daily lives. One of the hidden assumptions in IEs is that they propose a change of perspective in the relationships between humans and technology, shifting from a techno-centered perspective to a human-centered one. Unlike current computing systems where the user has to learn how to use the technology, an IE adapts its behaviour to the user, even anticipating his/her needs, preferences or habits. For that, the environment should learn how to react to the actions and needs of the users, and this should be achieved in an unobtrusive and transparent way. In order to provide personalized and adapted services, it is clear the need of knowing preferences and frequent habits of users. Thus, the ability to learn patterns of behaviour becomes an essential aspect for the successful implementation of IEs. In that sense, a perfect learning system would gain knowledge about everything related to users that would help the environment act intelligently and proactively. The efforts in this research work are focused on discovering frequent behaviours of the users. For that, it has been designed and developed the Learning Frequent Patterns of User Behaviour System (LFPUBS) that, taking into account all the particularities of IEs, learns frequent behaviours of the users. The core of the LFPUBS is the Learning Layer that unlike some other components is independent of the particular environment in which the system is being applied. On the one hand, it includes a language that allows the representation of discovered behaviours in a clear and unambiguous way. On the other hand, coupled with the language, an algorithm that discovers frequent behaviours has been designed and implemented. Finally, LFPUBS was validated using data collected from two real environments. Results obtained in such validation tests showed that LFPUBS was able to discover frequent behaviours of the users.