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Automated Multi-level Modeling

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Part of the Springer Tracts in Advanced Robotics book series (STAR,volume 93)

Abstract

Nearly all steps of a typical engineering process can be reliably automated. Software tools such as MathematicaTM, Coq, and BLAST can solve almost arbitrarily intricate equations, demonstrate complex theorems, and prove the correctness of algorithms. Machine tools and 3D printers can construct simple or complex objects based on a CAD model, with virtually no human intervention. Whenever a process can be broken into a set of relatively systematic and formal recipes, there is room for automation. Following this line of reasoning, and assuming that the MLMM provides such recipes, this chapter explores the opportunity of automating the generation of models at multiple abstraction levels. In particular, we refine and extend the MLMM by introducing a methodological and computational framework, called the M3 framework, which provides a consistent set of theoretical and computational tools targeted to the automated construction of models of SMPs at multiple abstraction levels.

Keywords

  • Control Mode
  • Model Reduction
  • Macroscopic Model
  • Control Space
  • Hybrid Automaton

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Grégory Mermoud .

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Mermoud, G. (2014). Automated Multi-level Modeling. In: Stochastic Reactive Distributed Robotic Systems. Springer Tracts in Advanced Robotics, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-319-02609-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-02609-1_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02608-4

  • Online ISBN: 978-3-319-02609-1

  • eBook Packages: EngineeringEngineering (R0)