Towards Argumentation-based Multiagent Induction

Authors:
Santiago Ontaòón;Enric Plaza
Affiliations:
IIIA-CSIC, Artificial Intelligence Research Institute Campus UAB, 08193 Bellaterra, Catalonia (Spain), {santi, enric}@iiia.csic.es;IIIA-CSIC, Artificial Intelligence Research Institute Campus UAB, 08193 Bellaterra, Catalonia (Spain), {santi, enric}@iiia.csic.es
Venue:
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Year:
2010

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Abstract

In this paper we propose an argumentation-based framework 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 cases 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 collection of rules. We show that the strategy converges to a hypothesis indistinguishable in training set accuracy from that learned by a centralized strategy.