Towards Virtual Experiment Laboratories: How Multi-Agent Simulations Can Cope with Multiple Scales of Analysis and Viewpoints

Servat, David; Perrier, Edith; Treuil, Jean-Pierre; Drogoul, Alexis

doi:10.1007/3-540-68686-x_20

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…microscopic) variables from microscopic (resp. macroscopic) processes, -grouping and degrouping (or aggregation and disaggregation) [6,20,26], i.e., defining a process at a level as a group (resp. part) of processes (resp.…”

Section: Multi-level Modelingmentioning

confidence: 99%

“…microscopic) variables from microscopic (resp. macroscopic) processes, -grouping and degrouping (or aggregation and disaggregation) [6,20,26],…”

Section: Multi-level Modelingmentioning

confidence: 99%

“…Other approaches such as IODA[10] or based on DEVS[18] are not described 3. Multi-level approaches have proven useful in many domain such as statistics[8],chemistry[9,11], physics[28], hydrology[26] or biology[33].…”

mentioning

confidence: 99%

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Engineering Hierarchical Complex Systems: An Agent-Based Approach. The Case of Flexible Manufacturing Systems

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Service Orientation in Holonic and Multi-Agent Manufacturing Control

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This article introduces a formal model to specify, model and validate hierarchical complex systems described at different levels of analysis. It relies on concepts that have been developed in the multi-agentbased simulation (MABS) literature: level, influence and reaction. One application of such model is the specification of hierarchical complex systems, in which decisional capacities are dynamically adapted at each level with respect to the emergences/constraints paradigm. In the conclusion, we discuss the main perspective of this work: the definition of a generic meta-model for holonic multi-agent systems (HMAS).Keywords: multi-level multi-agent based simulations, formal models, hierarchical systems 2 The presentation focuses on the influences → reaction model (IRM). Other approaches such as IODA [10] or based on DEVS [18] are not described. 3 Multi-level approaches have proven useful in many domain such as statistics [8], chemistry [9,11], physics [28], hydrology [26] or biology [33].

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Section: Multi-level Modelingmentioning

confidence: 99%

“…microscopic) variables from microscopic (resp. macroscopic) processes, -grouping and degrouping (or aggregation and disaggregation) [6,20,26],…”

Section: Multi-level Modelingmentioning

confidence: 99%