C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Teaching case-based argumentation through a model and examples
Teaching case-based argumentation through a model and examples
Formal systems for persuasion dialogue
The Knowledge Engineering Review
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
PADUA Protocol: Strategies and Tactics
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Argument Based Moderation of Benefit Assessment
Proceedings of the 2008 conference on Legal Knowledge and Information Systems: JURIX 2008: The Twenty-First Annual Conference
Arguments from Experience: The PADUA Protocol
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Proceedings of the 2010 conference on Computational Models of Argument: Proceedings of COMMA 2010
Multi-agent based classification using argumentation from experience
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
On the outcomes of multiparty persuasion
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multi-party Dialogue Games for Distributed Argumentation System
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
PISA: A framework for multiagent classification using argumentation
Data & Knowledge Engineering
On the outcomes of multiparty persuasion
ArgMAS'11 Proceedings of the 8th international conference on Argumentation in Multi-Agent Systems
Evaluating the Valuable Rules from Different Experience Using Multiparty Argument Games
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Applied Computational Intelligence and Soft Computing
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A framework, PISA, for conducting dialogues to resolve disputes concerning the correct categorisation of particular cases, is described. Unlike previous systems to conduct such dialogues, which have typically involved only two agents, PISA allows any number of agents to take part, facilitating discussion of cases which permit many possible categorizations. A particular feature of the framework is that the agents argue directly from individual repositories of experiences rather than from a previously engineered knowledge base, as is the usual case, and so the knowledge engineering bottleneck is avoided. Argument from experience is enabled by real time data-mining conducted by individual agents to find reasons to support their viewpoints, and critique the arguments of other parties. Multiparty dialogues raise a number of significant issues, necessitating appropriate design choices. The paper describes how these issues were resolved and implemented in PISA, and illustrates the operation of PISA using an example based on a dataset relating to nursery provision. Finally some experiments comparing PISA with other classifiers are reported.