Agents Retaining and Reusing of Experience Applied to Control of Semi-continuous Production Process

  • Gabriel Rojek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9692)


The model of human decision making involves reusing of earlier gathered experience together with retaining of knowledge related to currently undertaken decisions and its results. Presented here research focuses on the retaining of experience that should enable learning on the basis of results following currently undertaken actions. Such model of human decision making can be used as the basis by construction of a reasoning system in various application areas, one of which can be the control of a semi-continuous production process. Solutions in this application domain are presented, designed and implemented taking into account the paradigm of agent approach to design computer systems and the paradigm of case-base reasoning (CBR) as the methodology of solving present problems with the use of past made solutions.


The model of human decision making Case-base reasoning Semi-continuous production process Multi-agent systems 



Financial support of the Ministry of Science and Higher Education (AGH UST, project no. is acknowledged.


  1. 1.
    Sztangret, Ł., Rauch, Ł., Kusiak, J., Jarosz, P., Małecki, S.: Modeling of the oxidizing roasting process of zinc sulphide concentrates using the artificial neural networks. Comput. Methods Mater. Sci. 11, 122–127 (2011)Google Scholar
  2. 2.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Commun. 7, 39–59 (1994)Google Scholar
  3. 3.
    Richter, Michael M.: Introduction. In: Lenz, Mario, Burkhard, Hans-Dieter, Wess, Stefan, Bartsch-Spörl, Brigitte (eds.) Case-Based Reasoning Technology. LNCS (LNAI), vol. 1400, p. 1. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Bergmann, R., Althoff, K.D., Minor, M., Reichle, M., Bach, K.: Case-based reasoning - introduction and recent developments. Künstliche Intelligenz, German J. Artif. Intell. 23, 5–11 (2009)Google Scholar
  5. 5.
    Wooldridge, M.: An Introduction to MultiAgent Systems. Wiley, New York (2001)Google Scholar
  6. 6.
    Bequette, B.W.: Process Control: Modeling, Design and Simulation. Prentice Hall Press, Upper Saddle River (2003)Google Scholar
  7. 7.
    Rojek, G.: Agents modeling experience applied to control of semi-continuous production process. Comput. Sci. 15, 411–439 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

Personalised recommendations