The complexity of Multi-Agent Systems is constantly increasing. With the growth of the number of agents, interactions between them draw complex and huge conversations, i.e. sequences of messages exchanged inside the system. In this paper, we present a knowledge discovery process, mining those conversations to infer their underlying models, using stochastic grammatical inference techniques. We present some experiments that show the process we design is a good candidate to observe the interactions between the agents and infer the conversation models they build together.
['Arnaud Mounier', 'Olivier Boissier', 'François Jacquenet']
Conversation mining in multi-agent systems