Argumentation in Multi-Agent Systems : 4th International Workshop, ArgMAS 2007, Honolulu, HI, USA, May 15, 2007, Revised Selected and Invited Papers

Rahwan, Iyad (EDT); Parsons, Simon (EDT); Reed, Chris (EDT)

ISBN 10: 3540789146 / ISBN 13: 9783540789147

Edité par Springer, 2008

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Détails bibliographiques

• Titre: Argumentation in Multi-Agent Systems : 4th International Workshop, ArgMAS 2007, Honolulu, HI, USA, May 15, 2007, Revised Selected and Invited Papers
• Éditeur: Springer
• Date de parution: 2008
• Langue: anglais
• ISBN à 10 chiffres: 3540789146
• ISBN à 13 chiffres: 9783540789147
• Reliure: Couverture souple
• État de l'article: As New

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Synopsis

This volume presents the latest developments in the growing area of research at the interface of argumentation theory and multiagent systems. Argumentation provides tools for designing, implementing and analyzing sophisticated forms of interaction among rational agents. Application domains include: legal disputes, business negotiation, labor disputes, team formation, scientific inquiry, deliberative democracy, ontology reconciliation, risk analysis, scheduling, and logistics. The papers presented in this book constitute the thoroughly refereed post-workshop proceedings of the 4th International Workshop on Argumentation in Multi-Agent Systems, held in Honolulu, HI, USA, in May 2007 as an associated event of AAMAS 2007, the main international conference on autonomous agents and multi-agent systems. A number of invited revised papers on argumentation in MAS are also included, from both AAMAS 2007 and AAAI 2007, the 22nd Conference on Artificial Intelligence. The book has been divided into three parts, each addressing an important problem in argumentation and multiagent systems. The first two parts focus on issues pertaining to dialogue and on using argumentation to automate or support various single agent reasoning tasks. The third part addresses an exciting new area in argumentation research, namely, the relationship between models of argumentation and models of learning.