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Modeling Human Expertise on a Cheese Ripening Industrial Process Using GP

  • Olivier Barrière
  • Evelyne Lutton
  • Cedric Baudrit
  • Mariette Sicard
  • Bruno Pinaud
  • Nathalie Perrot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

Industrial agrifood processes often strongly rely on human expertise, expressed as know-how and control procedures based on subjective measurements (color, smell, texture), which are very difficult to capture and model. We deal in this paper with a cheese ripening process (of french Camembert), for which experimental data have been collected within a cheese ripening laboratory chain. A global and a monopopulation cooperative/coevolutive GP scheme (Parisian approach) have been developed in order to simulate phase prediction (i.e. a subjective estimation of human experts) from microbial proportions and Ph measurements. These two GP approaches are compared to Bayesian network modeling and simple multilinear learning algorithms. Preliminary results show the effectiveness and robustness of the Parisian GP approach.

Keywords

Bayesian Network Human Expertise Dynamic Bayesian Network Bayesian Network Modeling Kluyveromyces Marxianus 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Olivier Barrière
    • 1
  • Evelyne Lutton
    • 1
  • Cedric Baudrit
    • 2
  • Mariette Sicard
    • 2
  • Bruno Pinaud
    • 2
  • Nathalie Perrot
    • 2
  1. 1.INRIA Saclay - Ile-de-FranceParc Orsay UniversitéORSAY CedexFrance
  2. 2.UMR782 Génie et Microbiologie des Procédés Alimentaires., AgroParisTech, INRAThiverval-GrignonFrance

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