Dialectical argumentation with argumentation schemes: an approach to legal logic
Artificial Intelligence and Law - Law, logic and defeasibility
A study of accrual of arguments, with applications to evidential reasoning
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
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
Autonomous Agents and Multi-Agent Systems
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
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
| Hi-index | 0.00 |
This paper proposes a dialectical analysis model for multiparty argument games to evaluate rules mined from different past experience, called Arena. This model transforms the multiparty argument games into two-party argument games using the ideas from the Arena Contest of Chinese KungFu, to model a dynamic process of finding a defensible argument from the different agents' experience and to form a grid of dialectic analysis trees. For the same case, Arena enables the participating agents to propose their opinions and arguments, and provides them a platform to argue from experience in order to choose the valuable experience rule. When the training cases are more than enough, all the valuable rules about a set of databases will converge towards a stable set. We show how to choose the valuable experience rule from multiparty experience using Arena. This approach provides a new way to evaluate the experience rules mined from a database using argumentation. Arena demonstrates a fact that a combined analytical and inductive machine learning method could overcome the pitfalls associated with each separate approach.