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.
Document type :
Conference papers
Complete list of metadatas

https://hal-emse.ccsd.cnrs.fr/emse-00758357
Contributor : Florent Breuil <>
Submitted on : Wednesday, November 28, 2012 - 3:52:29 PM
Last modification on : Tuesday, October 23, 2018 - 2:36:08 PM

Identifiers

Citation

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, ⟨10.1145/860575.860801⟩. ⟨emse-00758357⟩

Share

Metrics

Record views

215