Argumentation-based Example Interchange for Multiagent Induction

Authors:
Santiago Ontaòón;Enric Plaza
Affiliations:
Artificial Intelligence Research Institute, IIIA-CSIC;Artificial Intelligence Research Institute, IIIA-CSIC
Venue:
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Year:
2010

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Abstract

Argumentation can be used by a group of agents to discuss about the validity of hypotheses. In this paper we propose an argumentation-based frame-work for multiagent induction, where two agents learn separately from individual training sets, and then engage in an argumentation process in order to converge to a common hypothesis about the data. The result is a multiagent induction strategy in which the agents minimize the set of examples that they have to exchange (using argumentation) in order to converge to a shared hypothesis. The proposed strategy works for any induction algorithm which expresses the hypothesis as a set of rules. We show that the strategy converges to a hypothesis indistinguishable in training set accuracy from that learned by a centralized strategy.