Advancing Profile Use in Agent Societies

摘要

Ubiquitous, persistent and pervasive computing systems and their modelling and engineering as agent societies suggest much potential for the application of personal and task profiles. Increasing quantities and types of profiling data are also becoming available. This paper discusses the use of profiles in this context and suggests a new form of profile employment to ease the use of profile data, by increasing convenience and privacy. Familiar dimension reduction techniques map the high dimensional profile to a position in a reduced dimension profile space. However, in this method, each profile owner calculates their position with respect to a number of reference vectors. This method avoids third party involvement and keeps the profile details private. Empirical studies indicate that calculating the whole set iteratively by this method compares favourably with respect to abstraction error when compared to direct calculation.