Knowledge-Based Planning for Industrial Automation Systems: The Way to Support Decision Making

  • N. R. Yusupbekov
  • Sh. M. Gulyamov
  • S. S. Kasimov
  • N. B. UsmanovaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)


The issue of adaptation of industrial automation system is considered in line with paradigm of multi-agent systems as a method of distributed artificial intelligence, wherein subsequent agents can be integrated into systems that jointly solve complex problems. In this regard we propose to use technique based on associative interrelation between the terms within multi-agents when piece of knowledge is represented as a function that maps a domain of clauses. While using trie-based structures (as possible representation technique) for good reasoning and decision making we can properly integrate metric information about the environment and semantic information provided by the user. In other words, we achieve another, higher level of representation of the environment when necessary knowledge is properly directed to actually reasoning (knowledge-based reasoning) and decision making.


Industrial automation system Associative interrelation Trie-based structure Knowledge-based reasoning 



The authors would like to acknowledge Sh. Narzullaev, Master of Telecommunication Engineering specialty, Tashkent University of Information Technologies for providing support in getting software codes examples according to task statement.


  1. 1.
    Wooldridge, M.: An Introduction to Multiagent Systems. Wiley, Hoboken (2002)Google Scholar
  2. 2.
    Weiss, G.: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge (1999)Google Scholar
  3. 3.
    Usmanova, N.: Agent-based modeling of distributed environment: assigning properties to agent. In: Eighth World Conference on Intelligent Systems for Industrial Automation, Proceedings, WCIS 2014, Tashkent (2014)Google Scholar
  4. 4.
    Nissim, R., Brafman, R., Domshlak, C.: A general, fully distributed multi-agent planning algorithm. In: ISF Research/2007, William Frankel Center for Computer Science (2007)Google Scholar
  5. 5.
    Obitko, M., Marik, V.: Ontologies for multi-agent systems in manufacturing domain. In: DEXA 2002: Proceedings of the 13th International Workshop on Database and Expert Systems Applications, p. 597. IEEE Computer Society, Washington, DC (2002)Google Scholar
  6. 6.
    Cheung, C.F., Wang, W.M., Kwok, S.K.: Knowledge-based inventory management in production logistics: a multi-agent approach. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 219(3), 299–308 (2005)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Melik-Merkumians, M., Moser, T., Schatten, A., Zoitl, A., Biffl, S.: Knowledge-based Runtime Failure Detection for Industrial Automation Systems.
  9. 9.
    Moser, T., Mordinyi, R., Winkler, D.: Efficient automation systems engineering process support based on semantic integration of engineering knowledge.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. R. Yusupbekov
    • 1
  • Sh. M. Gulyamov
    • 1
  • S. S. Kasimov
    • 2
  • N. B. Usmanova
    • 2
    Email author
  1. 1.Tashkent State Technical UniversityTashkentUzbekistan
  2. 2.Tashkent University of Information TechnologiesTashkentUzbekistan

Personalised recommendations