Neural networks and open texture
ICAIL '93 Proceedings of the 4th international conference on Artificial intelligence and law
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Team Formation by Self-Interested Mobile Agents
Selected Papers from the 4th Australian Workshop on Distributed Artificial Intelligence, Multi-Agent Systems: Theories, Languages, and Applications
Formal systems for persuasion dialogue
The Knowledge Engineering Review
Argument based machine learning applied to law
Artificial Intelligence and Law - Argumentation in artificial intelligence and law
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
Arguments from Experience: The PADUA Protocol
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Multi-party argument from experience
ArgMAS'09 Proceedings of the 6th international conference on Argumentation in Multi-Agent Systems
Multi-agent based classification using argumentation from experience
Autonomous Agents and Multi-Agent Systems
Argue to agree: A case-based argumentation approach
International Journal of Approximate Reasoning
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We have previously introduced the notion of arguing from experience, whereby agents debate a classification problem using arguments based on association rules mined “on the fly” from their individual datasets. In this paper we extend PISA, which allows for n agents to argue about cases which have n possible classifications. By allowing any number of agents to participate all the agents supporting a given classification can form a collaborative group for the purposes of the dialogue. We describe how the system is organised, give an example, and report results which suggest that allowing groups in this way has a beneficial effect on the quality of the result.