Abstract
The complexity of Multi-Agent Systems is constantly increasing. With the growth of the number of agents, interactions between them draw complexan d 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.
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Mounier, A., Boissier, O., Jacquenet, F. (2003). Conversation Mining in Multi-agent Systems. In: Mařík, V., Pěchouček, M., Müller, J. (eds) Multi-Agent Systems and Applications III. CEEMAS 2003. Lecture Notes in Computer Science(), vol 2691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45023-8_16
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DOI: https://doi.org/10.1007/3-540-45023-8_16
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