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Conference proceeding
Argumentation-based Example Interchange for Multiagent Induction
ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT, v 220, pp 59-68
01 Jan 2010
Featured in Collection : UN Sustainable Development Goals @ Drexel
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 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.
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Details
- Title
- Argumentation-based Example Interchange for Multiagent Induction
- Creators
- Santiago Ontanon - Artificial Intelligence Research InstituteEnric Plaza - Artificial Intelligence Research Institute
- Contributors
- R Alquezar (Editor)A Moreno (Editor)J Aguilar (Editor)
- Publication Details
- ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT, v 220, pp 59-68
- Series
- Frontiers in Artificial Intelligence and Applications
- Publisher
- Ios Press
- Number of pages
- 10
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000321818400006
- Scopus ID
- 2-s2.0-78049262527
- Other Identifier
- 991021869114704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications