By Nicolas Maudet, Simon D. Parsons, Iyad Rahwan
Argumentation presents instruments for designing, enforcing and interpreting refined varieties of interplay between rational agents. It has made an excellent contribution to the perform of multiagent dialogues. program domain names comprise: criminal disputes, company negotiation, exertions disputes, workforce formation, medical inquiry, deliberative democracy, ontology reconciliation, danger research, scheduling, and logistics.
This ebook constitutes the completely refereed post-proceedings of the 3rd foreign Workshop on Argumentation in Multi-Agent structures held in Hakodate, Japan, in may possibly 2006 as an linked occasion of AAMAS 2006, the most overseas convention on self sustaining brokers and multi-agent systems.
The quantity opens with an unique cutting-edge survey paper providing the present examine and supplying a complete and updated assessment of this speedily evolving quarter. The eleven revised articles that keep on with have been conscientiously 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 eu convention on synthetic Intelligence.
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Additional resources for Argumentation in Multi-Agent Systems: Third International Workshop, ArgMAS 2006, Hakodate, Japan, May 8, 2006, Revised Selected and Invited Papers
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. Speciﬁcally, the argument shown in Figure 4 endorses the solution hadromerida for a particular problem P . The justiﬁcation D1 in that argument can be interpreted saying that “the prediction for P is hadromerida because the smooth form of the megascleres of the spikulate skeleton of the sponge is of type tylostyle, the spikulate skeleton has no uniform length, and there is no gemmules in the external features of the sponge”.
Thus, learning agents that are capable of arguing their individual predictions with other agents may reach better prediction accuracy after such an argumentation process. N. Maudet, S. Parsons, and I. ): ArgMAS 2006, LNAI 4766, pp. 36–53, 2007. c Springer-Verlag Berlin Heidelberg 2007 Arguments and Counterexamples in Case-Based Joint Deliberation 37 In this paper we address the issue of joint deliberation among two learning agents using an argumentation framework. Our assumptions are that these two agents work in the same domain using a shared ontology, they are capable of learning from examples, and they interact following a speciﬁc interaction protocol.
Figure 3 illustrates this idea. c) β is a counterargument of α, and is more speciﬁc than α. c) both arguments endorse the same solution, and thus β is not a counterargument of α. The generation of counterarguments using the speciﬁcity criterion imposes some restrictions over the learning method, although LID or ID3 can be easily adapted to generate counterarguments. For instance, LID is an algorithm that generates a description starting by the empty term and heuristically adding features to that term.