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, enforcing and interpreting refined kinds of interplay between rational brokers. It has made a superb contribution to the perform of multiagent dialogues. software domain names contain: felony disputes, enterprise negotiation, hard work disputes, workforce formation, clinical inquiry, deliberative democracy, ontology reconciliation, threat 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 could 2006 as an linked occasion of AAMAS 2006, the most foreign convention on self reliant brokers and multi-agent systems.

The quantity opens with an unique cutting-edge survey paper providing the present learn and delivering a finished and up to date evaluation of this quickly evolving sector. The eleven revised articles that stick to 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 resources for Argumentation in Multi-Agent Systems: Third International Workshop, ArgMAS 2006 Hakodate, Japan, May 8, 2006 Revised Selected and Invited Papers

Example text

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. Example 6. Let T = R[0, 1] be a complete lattice of the unit interval of real numbers.

JA is preserved by knowledge acquisition induced by difference of recognition. Example 3. Let T = FOUR, and M AS = {KBA , KBB }, where Agent A and B have the following knowledge bases on the evaluation of a movie. KBA = { recommend(movie) : t ← f amous(actor) : t & f amous(story) : t, f amous(actor) : t ←, f amous(story) : t ← }, KBB = { recommend(movie) : f ← poor(actor) : t & see(movie) : t, poor(actor) : t ←, see(movie) : t ← }. Suppose they put forward the arguments ArgA and ArgB respectively.

If such a case c is found, then c is sent to the other agent as a counterexample of α. 3. If no counterexamples are found, then Ai cannot rebut the argument α.

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