Abstract | ||
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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. |
Year | DOI | Venue |
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2010 | 10.3233/978-1-60750-606-5-1111 | ECAI |
Keywords | Field | DocType |
strategy converges,argumentation process,towards argumentation-based multiagent induction,multiagent induction,multiagent induction strategy,shared hypothesis,induction algorithm,common hypothesis,individual training set,centralized strategy,proposed strategy work | Training set,Computer science,Argumentation theory,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
215 | 0922-6389 | 1 |
PageRank | References | Authors |
0.35 | 6 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Santiago Ontaòón | 1 | 5 | 1.47 |
Enric Plaza | 2 | 1 | 0.69 |