Multi-Agent Behaviour Segmentation via Spectral Clustering

Balint Takacs, Simon Butler,Yiannis Demiris

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 propose 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.

Subjects: 7.1 Multi-Agent Systems; 1.11 Planning

Submitted: May 15, 2007


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