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 great contribution to the perform of multiagent dialogues. software domain names contain: criminal disputes, company negotiation, exertions disputes, group formation, clinical inquiry, deliberative democracy, ontology reconciliation, probability research, scheduling, and logistics.

This publication constitutes the completely refereed post-proceedings of the 3rd overseas Workshop on Argumentation in Multi-Agent platforms held in Hakodate, Japan, in may well 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 featuring the present study and providing a accomplished and up to date evaluate of this quickly 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 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

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If Argi ∈ JA and Argk ∈ JA, agent i updates KBi to KBi = KBi ∪ArgKBk , and agent k updates KBk to KBk = KBk ∪ ArgKBi ; 2. if ArgKBi ∈ JA and ArgKBk ∈ JA, then agent k updates KBk to KBk = KBk ∪ ArgKBi ; 3. if ArgKBi ∈ JA and ArgKBk ∈ JA, then agent i and k do not learn anything, resulting in no updates on their knowledge bases. Argumentation-Based Learning 27 Under this definition, agents or agents’ attitude toward update are supposed to be credulous in the sense that they update their knowledge bases as far as arguments are justified.

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 ← }.

Definition 16 (dialogue [11]). An dialogue is a finite nonempty sequence of moves movei = (P layeri , Argi ), (i ≥ 1) such that 1. P layeri = P (Proponent) ⇔ i is odd; and P layeri = O (Opponent) ⇔ i is even. 2. If P layeri = P layerj = P (i = j) then Argi = Argj . 3. If P layeri = P (i ≥ 3) then (Argi , Argi−1 ) ∈ su; and if P layeri = O (i ≥ 2) then (Argi , Argi−1 ) ∈ d. In this definition, it is permitted that P = O, that is a dialogue is done by only one agent. Then, we say such an argument is a self-argument.

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