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Handling Agents’ Incomplete Information in a Coalition Formation Model

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Date
2014
Dewey
Intelligence artificielle
Sujet
Multiagent System; Coalition Formation; Coalition Structure; Probabilistic Strategy; Coalition Evaluation
DOI
http://dx.doi.org/10.1007/978-3-319-30307-9_4
Conference name
7th International Workshop on Agent-based Complex Automated Negotiation, ACAN 2014
Conference date
05-2014
Conference city
Paris
Conference country
France
Book title
Recent Advances in Agent-based Complex Automated Negotiation
Author
Fukuta, Naoki; Ito, Takayuki; Zhang, Minjie; Fujita, Katsuhide; Robu, Valentin
Publisher
Springer
Publisher city
Berlin Heidelberg
ISBN
978-3-319-30305-5
Book URL
10.1007/978-3-319-30307-9
URI
https://basepub.dauphine.fr/handle/123456789/21122
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Arib, Souhila
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Aknine, Samir
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Genin, Thomas
115536 autre
Type
Communication / Conférence
Item number of pages
55-70
Abstract (EN)
Coalition formation is a problem of great interest in AI, allowing groups of autonomous rational agents to form suitable teams. Our work specially focuses on agents which are self-interested and want to negotiate for executing actions in their plans. Depending on its capabilities, an agent may not be able to perform actions alone. Then the agent needs to find partners, interested in the same actions, and agree to put their resources in common, in order to perform these actions all together. We propose in this paper a coalition formation mechanism based on: (1) an action selection algorithm, which allows an agent to select the actions to propose and deal with the incomplete information about other agents in the system and (2) a coalition evaluation algorithm, which allows an agent to select a group of agents to perform with these actions. Our coalition evaluation algorithm is designed for structured-preference context, based on the use of the information gathered in the previous interactions with other agents. It allows the agents to select partners, which are more likely interested in the actions. These algorithms are detailed and exemplified. We have studied the quality of the solution, we have implemented and tested them, and we provide the results of their evaluation.

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