Multi-Agent Joint Learning From Argumentation
ADMI 2013: Revised Selected Papers of the 9th International Workshop on Agents and Data Mining Interaction - Volume 8316(2014)
摘要
Joint learning from argumentation is the idea that groups of agents with different individual knowledge take part in argumentation to communicate with each other to improve their learning ability. This paper focuses on association rule, and presents MALA, a model for argumentation based multi-agent joint learning which integrates ideas from machine learning, data mining and argumentation. We introduce the argumentation model Arena as a communication platform with which the agents can communicate their individual knowledge mined from their own datasets. We experimentally show that MALA can get a shared and agreed knowledge base and improve the performance of association rule mining.
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关键词
Argumentation,Data mining,Association rule,Multi-agent learning
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