Multi-Agent Behaviour Segmentation via Spectral Clustering

We examine the application of spectral clustering for breaking up the behaviour of a multi-agent system in space and time into smaller, independent elements. We extend the clustering into the temporal domain and pro- pose a novel similarity measure, which is shown to possess desirable temporal properties when clustering multi-agent behaviour. We also propose a technique to add knowledge about events of multi-agent interaction with different importance. We apply spectral clustering with this measure for analysing behaviour in a strategic game.