Argumentation in Multi-Agent Systems: Third International by Nicolas Maudet, Simon D. Parsons, Iyad Rahwan
By Nicolas Maudet, Simon D. Parsons, Iyad Rahwan
Argumentation presents instruments for designing, imposing and interpreting subtle kinds of interplay between rational brokers. It has made a fantastic contribution to the perform of multiagent dialogues. program domain names contain: criminal disputes, enterprise negotiation, exertions disputes, staff formation, medical inquiry, deliberative democracy, ontology reconciliation, hazard research, scheduling, and logistics.
This e-book constitutes the completely refereed post-proceedings of the 3rd overseas Workshop on Argumentation in Multi-Agent platforms held in Hakodate, Japan, in could 2006 as an linked occasion of AAMAS 2006, the most foreign convention on self sufficient brokers and multi-agent systems.
The quantity opens with an unique state of the art survey paper proposing the present study and providing a complete and updated evaluate of this speedily evolving sector. The eleven revised articles that persist with have been rigorously reviewed and chosen from the main major workshop contributions, augmented with papers from the AAMAS 2006 major convention, in addition to from ECAI 2006, the biennial ecu convention on synthetic Intelligence.
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Extra info for Argumentation in Multi-Agent Systems: Third International Workshop, ArgMAS 2006 Hakodate, Japan, May 8, 2006 Revised Selected and Invited Papers
We describe a dynamically changing argument example in which agents are involved in not only a single argument at a time but a process of consecutive arguments over time, and agents gradually become wise through them. This suggests an interesting and important direction to which argumentation studies head from now since acquisition not only ends once and for all, but also it continues repeatedly every time new information are found and added, and new agents appear. Similar observation can be seen in dialectic development of thought, society and so on in philosophy, and social processes of scientiﬁc development in philosophy of science.
Notice that to obtain the solution class, the decision tree has just used the value of one attribute, traﬃc light. Therefore, the justiﬁcation must contain only the attribute/value pair shown in the ﬁgure. The values of the rest of attributes are irrelevant, since whatever their value the solution class would have been the same. e. all the cases that are subsumed by the justiﬁcation) belong to the predicted solution class. In the rest of the paper, we will use to denote the subsumption relation.
Using the previously deﬁned conﬁdence measure, the preference relation used in our framework is the following one: a justiﬁed prediction α is preferred over another one β is C(α) ≥ C(β). 5 Generation of Arguments In our framework, arguments are generated by the agents using CBR algorithms. However, any learning method able to provide a justiﬁed prediction can be used to generate arguments. In particular, we use the LID CBR method . 1. For instance, Figure 4 shows an argument generated by LID in the sponge data set, used in our experiments.