Mining Qualitative Context Models from Multiagent Interactions (Extended Abstract)

We present a novel method for analysing the behaviour of multiagent systems on the basis of the semantically rich information provided by agent communication languages and interaction protocols. Contrary to analysis methods that rely on observing more low-level patterns of behaviour [3, 4], our method is based on exploiting the semantics. These languages and protocols which can be used to extract qualitative properties of observed interactions. This can be achieved by interpreting the logical constraints associated with protocol execution paths or individual messages as models of the context of an observed interaction, and using them as features of learning samples.