It is important that systems that exhibit proactive behaviour do so in a way that does not surprise or frustrate the user. Consequently, it is desirable for such systems to be both personalised and designed in such a way as to enable the user to scrutinise her user model (part of which should hold the rules describing the behaviour of the system). This article describes on-going work to investigate the design of a prototype system that can learn a given user’s behaviour in an office environment in order to use the inferred rules to populate a user model and support appropriate proactive behaviour (e.g. turning on the user’s fan under appropriate conditions). We explore the tension between user control and proactive services and consider issues related to the design of appropriate transparency with a view to supporting user comprehensibility of system behaviour. To this end, our system enables the user to scrutinise and possibly over-ride the ‘IF-THEN’ rules held in her user model. The system infers these rules from the context history (effectively a data set generated using a variety of sensors) associated with the user by using a fuzzy-decision-tree-based algorithm that can provide a confidence level for each rule in the user model. The evolution of the system has been guided by feedback from a number of real-life users in a university department. A questionnaire study has yielded supplementary results concerning the extent to which the approach taken meets users’ expectations and requirements.
This paper presents original work investigating the tension between intelligent systems and user control. The described prototype system learns user's behaviour in an office environment, infers rules to populate a user model and supports appropriate proactive behaviour which enables user understanding of system behaviour. It particularly exploits the system's transparency through enabling user to scrutinise and over-ride the rules held in the user model. In 2003, the UMUAI journal has been ranked 6 among 451 computer science journals in the ISI database, and in 2005 its impact factor was 1.318. RAE_import_type : Journal article RAE_uoa_type : Computer Science and Informatics