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ANEMONA [electronic resource] : A Mulit-agent Methodology for Holonic Manufacturing Systems / by Vicente Botti, Adriana Giret.

By: Contributor(s): Material type: TextTextSeries: Springer Series in Advanced ManufacturingPublisher: London : Springer London, 2008Description: XVI, 214 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781848003101
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 670 23
LOC classification:
  • TJ241
Online resources:
Contents:
Backgrounds -- Holonic Manufacturing Systems -- Holons and Agents -- Methodology for Holonic Manufacturing System -- HMS Development -- ANEMONA Notation -- ANEMONA Development Process -- Evaluation and Case Study -- Evaluation of the ANEMONA Methodology -- Case Study -- Conclusions.
In: Springer eBooksSummary: ANEMONA is a multi-agent system (MAS) methodology for holonic manufacturing system (HMS) analysis and design, based on HMS requirements. ANEMONA defines a mixed top-down and bottom-up development process, and provides HMS-specific guidelines to help the designer in identifying and implementing holons. In ANEMONA, the specified HMS is divided into concrete aspects that form different "views" of the system. The development process of ANEMONA provides clear and HMS-specific modeling guidelines for HMS designers, and complete development phases for the HMS life cycle. The analysis phase is defined in two stages: System Requirements Analysis, and Holon Identification and Specification. This analysis provides high-level HMS specifications from the requirements, adopting a top-down recursive approach. An advantage of this recursive analysis is that its results, i.e. the analysis models, provide a set of elementary elements and assembling rules. The next stage is Holon Design, a bottom-up process to produce the system architecture from the analysis models of the previous stage. The Holons Implementation stage produces an Executable Code for the SetUp and Configuration stage. Finally, maintenances functions are executed in the Operation and Maintenance stage. ANEMONA: A Multi-agent Methodology for Holonic Manufacturing Systems will be of interest to researchers and postgraduate students involved in artificial intelligence and software engineering, as well as to manufacturing engineers in industry and academia.
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Backgrounds -- Holonic Manufacturing Systems -- Holons and Agents -- Methodology for Holonic Manufacturing System -- HMS Development -- ANEMONA Notation -- ANEMONA Development Process -- Evaluation and Case Study -- Evaluation of the ANEMONA Methodology -- Case Study -- Conclusions.

ANEMONA is a multi-agent system (MAS) methodology for holonic manufacturing system (HMS) analysis and design, based on HMS requirements. ANEMONA defines a mixed top-down and bottom-up development process, and provides HMS-specific guidelines to help the designer in identifying and implementing holons. In ANEMONA, the specified HMS is divided into concrete aspects that form different "views" of the system. The development process of ANEMONA provides clear and HMS-specific modeling guidelines for HMS designers, and complete development phases for the HMS life cycle. The analysis phase is defined in two stages: System Requirements Analysis, and Holon Identification and Specification. This analysis provides high-level HMS specifications from the requirements, adopting a top-down recursive approach. An advantage of this recursive analysis is that its results, i.e. the analysis models, provide a set of elementary elements and assembling rules. The next stage is Holon Design, a bottom-up process to produce the system architecture from the analysis models of the previous stage. The Holons Implementation stage produces an Executable Code for the SetUp and Configuration stage. Finally, maintenances functions are executed in the Operation and Maintenance stage. ANEMONA: A Multi-agent Methodology for Holonic Manufacturing Systems will be of interest to researchers and postgraduate students involved in artificial intelligence and software engineering, as well as to manufacturing engineers in industry and academia.

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