ABSG : une architecture d’agents d’inspiration sociale pour le problème de formation de coalitions
Revue Ouverte d'Intelligence Artificielle, Volume 4 (2023) no. 2, pp. 9-40.

Nous proposons une nouvelle architecture d’agent d’inspiration sociale adaptée à un système d’aide à la décision pour résoudre un problème de génération de structures de coalitions distribué avec chevauchements pour la conception de produits dans le cadre de l’économie circulaire. Cette architecture centrée agent permet aux agents de savoir avec quelles accointances former une coalition de manière à designer des produits répondant au mieux à un besoin utilisateur. Le mécanisme cognitif utilisé par l’architecture ABSG s’inspire des principes de l’attraction des sciences humaines et sociales.

We propose a new socially inspired agent architecture adapted to a decision support system to solve a distributed problem of coalitions structure generation for product design in the circular economy. This agent-centric architecture allows agents to know with whom to form a coalition. The cognitive mechanism used by the ABSG architecture is inspired by the principles of attraction from the human and social sciences. We argue that a multi-agent paradigm and a social approach are suitable solutions for solving open and dynamic coalition formation problems. We assess this assumption by using a study case from industry

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Accepté le :
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DOI : 10.5802/roia.55
Mot clés : Formation de coalitions, système multi-agent, architecture d’agent.
Keywords: Isotriviality, log-selfishness, Machine.
Mickaël Bettinelli 1 ; Michel Occello 2 ; Damien Genthial 

1 Université Savoie Mont Blanc, LISTIC, Annecy, France
2 Grenoble Alpes University, LCIS, 26000 Valence, France
Licence : CC-BY 4.0
Droits d'auteur : Les auteurs conservent leurs droits
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     journal = {Revue Ouverte d'Intelligence Artificielle},
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Mickaël Bettinelli; Michel Occello; Damien Genthial. ABSG : une architecture d’agents d’inspiration sociale pour le problème de formation de coalitions. Revue Ouverte d'Intelligence Artificielle, Volume 4 (2023) no. 2, pp. 9-40. doi : 10.5802/roia.55. https://roia.centre-mersenne.org/articles/10.5802/roia.55/

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