How to learn to interact?

Abstract : 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. In this paper, we present a knowledge discovery process based on conversation mining and used to infer conversation models. Conversations are made up of sequences of messages exchanged inside the system. In order to discover the underlying conversation models that agents use while interacting with each other, we apply stochastic grammatical inference techniques on the conversations. We present some experiments that show the process we design is a good candidate to equip agents with the capacity to learn to interact.
Type de document :
Communication dans un congrès
Second international joint conference on Autonomous agents and multiagent systems, 2003, New York, United States. pp. 1072-1073, 2003, 〈10.1145/860575.860801〉
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https://hal-emse.ccsd.cnrs.fr/emse-00758357
Contributeur : Florent Breuil <>
Soumis le : mercredi 28 novembre 2012 - 15:52:29
Dernière modification le : jeudi 11 janvier 2018 - 06:20:35

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Arnaud Mounier, Olivier Boissier, François Jacquenet. How to learn to interact?. Second international joint conference on Autonomous agents and multiagent systems, 2003, New York, United States. pp. 1072-1073, 2003, 〈10.1145/860575.860801〉. 〈emse-00758357〉

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