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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 types of interplay between rational agents. It has made an outstanding contribution to the perform of multiagent dialogues. software domain names comprise: felony disputes, enterprise negotiation, hard work disputes, staff formation, medical inquiry, deliberative democracy, ontology reconciliation, possibility 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 well 2006 as an linked occasion of AAMAS 2006, the most overseas convention on self sufficient brokers and multi-agent systems.

The quantity opens with an unique cutting-edge survey paper featuring the present study and providing a accomplished and updated review of this swiftly evolving region. The eleven revised articles that stick 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 man made 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

Example text

We focus on argumentation between two agents, presenting (1) an interaction protocol (AMAL2) that allows agents to learn from counterexamples and (2) a preference relation to determine the joint outcome when individual predictions are in contradiction. We present several experiment to asses how joint predictions based on argumentation improve over individual agents prediction. 1 Introduction Argumentation frameworks for multiagent systems can be used for different purposes like joint deliberation, persuasion, negotiation, and conflict resolution.

Rm ] such that r1k =∼ A : μ2 ← Lk1 & . . & Lknk , and A : μ1 and ∼ A : μ2 rebut each other. Then, 1. if Argi ∈ JA rebuts Argk ∈ JA, then KBk = KBk ∪ Argi \ r1k ; 2. if Argi ∈ JA rebuts Argk ∈ JA, then agent i makes an agreed composite argument ACA from Argi and Argk , and KBi = KBi ∪ ACA \ {r1i }; 3. if Argi ∈ JA rebuts Argk ∈ JA, agents i and k do not learn anything, resulting in no change in their knowledge bases. Example 5. , 10}, and M AS = {KBA , KBB , KBC }, where KBA = { recommend(movie) : 8 ← good story : 9 & not expensive(movie) : 7, good story : 9 ← }, KBB = {∼ recommend(movie) : 2 ← skilled actor : 3, skilled actor : 3 ← }, KBC = {recommend(movie) : 1 ← expensive(movie) : 8, expensive(movie) : 8 ← }.

S, the higher the confidence will be. D that do not belong to that solution class. When two agents A1 and A2 want to assess the confidence on a justified prediction α made by one of them, each of them examine the prediction and sends the aye and nay values obtained to the other agent. e. the confidence on a justified prediction is the number of endorsing cases divided by the number of endorsing cases plus counterexamples found by each of the two agents. The reason for adding one to the numerator and 2 to the 42 S.

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