Michael Schroeder
ABSTRACT
Interesting features of complex agent systems can be captured as multivariate data. There are a number of different approaches to visualizing such data. In this paper, we focus on methods which reduce the dimensions of the data through matrix transformations and then visualise the entities in the lower-dimensional space.
We review an approach which describes agent similarities through distances, which are then visualised by multi-dimensional scaling techniques. We point out some shortcomings of this approach and examine an alternative, which applies principal component analysis and subsequent visualisation directly to the data. Our approach is implemented in the Space Explorer tool, which also allows interactive exploration.
We identify four categories of data, which capture interaction, profiles, time series, and combinations of these three. Then we consider how to employ them for various agent types such as communicating, mobile, personal, interface, information and collaborating agents. Finally, we examine real-world telecoms data of 90,000 calls with Space Explorer.
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Index Terms
- Multi-agent visualisation based on multivariate data
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