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Título: | Argumentation-based Example Interchange for Multiagent Induction |
Autor: | Ontañón, Santiago CSIC; Plaza, Enric CSIC ORCID | Palabras clave: | Multiagent learning Induction Argumentation |
Fecha de publicación: | 2010 | Editor: | IOS Press | Citación: | Artificial Intelligence Research and Development 220: 59- 68 (2010) | Resumen: | Argumentation can be used by a group of agents to discuss about the validity of hypotheses. 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 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 | URI: | http://hdl.handle.net/10261/243425 |
Aparece en las colecciones: | (IIIA) Libros y partes de libros |
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Argumentation-based Example.pdf | 492,85 kB | Adobe PDF | Visualizar/Abrir |
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