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pyDCOP, a DCOP library for IoT and dynamic systems

Authors :
Rust, Pierre
Picard, Gauthier
Ramparany, Fano
Orange Labs R&D [Rennes]
France Télécom
Laboratoire Hubert Curien [Saint Etienne] (LHC)
Institut d'Optique Graduate School (IOGS)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)
École des Mines de Saint-Étienne (Mines Saint-Étienne MSE)
Institut Mines-Télécom [Paris] (IMT)
Institut Henri Fayol (FAYOL-ENSMSE)
Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)
Département Informatique et systèmes intelligents ( FAYOL-ENSMSE)
Ecole Nationale Supérieure des Mines de St Etienne
France Télécom Recherche et Développement (FTR&D)
Source :
International Workshop on Optimisation in Multi-Agent Systems (OptMAS@AAMAS 2019), International Workshop on Optimisation in Multi-Agent Systems (OptMAS@AAMAS 2019), May 2019, Montréal, Canada
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

Abstract

International audience; This demonstration illustrates the newly developed Python-based framework, pyDCOP, which implements several state-of-the-art distributed constraint reasoning solution methods, provides utilities to deploy them over distributed infrastructures and also equip the system with resilience capabilities.The idea behind pyDCOP is to distribute agents over an Internet-of-Things infrastructure (e.g. Rapsberry Pis) to install collective decisions, as to implement Ambient Intelligence or Smart Home scenarios. Scenarios are modeled in a dedicated format, translated in a distributed constraint optimization or satisfaction problem, then pushed to the devices which coordinate using chosen protocols as to self-configure in a decentralized manner. Besides configuring the system in an optimal manner, it also provides a resilience framework, which equips the system with adaptation capabilities against unpredictable device removals. This mechanism is based on decision replication and a lightweight DCOP-based reparation mechanism.

Details

Language :
English
Database :
OpenAIRE
Journal :
International Workshop on Optimisation in Multi-Agent Systems (OptMAS@AAMAS 2019), International Workshop on Optimisation in Multi-Agent Systems (OptMAS@AAMAS 2019), May 2019, Montréal, Canada
Accession number :
edsair.dedup.wf.001..c7a378145a7a063c6b835429aae3620b