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Towards Autonomous AI Systems for Resource Management: Applications in Industry and Lessons Learned

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Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection (PAAMS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10978))

Abstract

Complexity of modern resource management is analyzed and related with a number of decision makers, high variety of individual criteria, preferences and constraints, interdependency of all operations, etc. The overview of existing methods and tools of Enterprise Resource Planning is given and key requirements for resource management are specified. The concept of autonomous Artificial Intelligence (AI) systems for adaptive resource management based on multi-agent technology is discussed. Multi-agent model of virtual market and method for solving conflicts and finding consensus for adaptive resource management are presented. Functionality and architecture of autonomous AI systems for adaptive resource management and the approach for measuring adaptive intelligence and autonomy level in these systems are considered. Results of delivery of autonomous AI solutions for managing trucks and factories, mobile teams, supply chains, aerospace and railways are presented. Considerable increase of enterprise resources efficiency is shown. Lessons learned from industry applications are formulated and future developments of AI for solving extremely complex problems of adaptive resource management are outlined.

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References

  1. Capitalizing on Complexity? Insights from the Global Chief Executive Officer Study, IBM, USA (2010). http://www-01.ibm.com/industries/.government/ieg/pdf/CEOstudy_2010_GovernmentFocus.pdf. Accessed 8 Feb 2018

  2. Skobelev, P., Trentesaux, D.: Disruptions are the norm: cyber-physical multi-agent systems for autonomous real-time resource management. In: Borangiu, T., Trentesaux, D., Thomas, A., Leitão, P., Barata Oliveira, J. (eds.) Service Orientation in Holonic and Multi-Agent Manufacturing. SCI, vol. 694, pp. 287–294. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51100-9_25

    Chapter  Google Scholar 

  3. Gartner: Top Strategic Predictions for 2016 and Beyond: The Future is a Digital Thing (2016). https://www.gartner.com/doc/3142020?refval=&pcp=mpe

  4. Artificial Intelligence and Robotics – Imperial College, UK-RAS White Paper, 48 p. (2017). http://hamlyn.doc.ic.ac.uk/uk-ras/sites/default/files/UK_RAS_wp_AI_web_retina.pdf

  5. Rzevski, G., Skobelev, P.: Managing Complexity. WIT Press, London-Boston (2014)

    Google Scholar 

  6. Leung, J.: Handbook of Scheduling: Algorithms, Models and Performance Analysis. CRC Computer and Information Science Series. Chapman & Hall, London (2004)

    MATH  Google Scholar 

  7. Voß, S.: Meta-heuristics: the state of the art. In: Nareyek, A. (ed.) LSPS 2000. LNCS (LNAI), vol. 2148, pp. 1–23. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45612-0_1

    Chapter  Google Scholar 

  8. Binitha, S., Siva Sathya, S.: A survey of bio inspired optimization algorithms. Int. J. Soft Comput. Eng. (IJSCE) 2(2), 137–151 (2012)

    Google Scholar 

  9. Rossi, F., van Beek, P., Walsh, T.: Handbook of Constraint Programming. Elsevier Science, New York (2006)

    MATH  Google Scholar 

  10. Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, Cambridge (2010). http://www.cs.cornell.edu/home/kleinber/networks-book/. Accessed 30 Jan 2018

    Book  Google Scholar 

  11. Shoham, Y., Leyton-Brown, K.: Multi-agent Systems: Alghoritmic, Game Theoretic and Logical Foundations. Cambridge University Press, Cambridge (2009)

    MATH  Google Scholar 

  12. Brussel, H.V., Wyns, J., Valckenaers, P., Bongaerts, L.: Reference architecture for holonic manufacturing systems: PROSA. Comput. Ind. 37(3), 255–274 (1998)

    Article  Google Scholar 

  13. Skobelev, P.: Open multi-agent systems for decision-making support. Avtometriya, J. Sib. Branch Russ. Acad. Sci. 6, 45–61 (2002)

    Google Scholar 

  14. Skobelev, P., Vittikh, V.: Models of self-organization for demand-resource networks, automation and control. J. Rus. Acad. Sci. 1, 177–185 (2003)

    Google Scholar 

  15. Vittikh, V., Skobelev, P.: The compensation method of agents interactions for real time resource allocation. Avtometriya, J. Sib. Branch Russ. Acad. Sci. 2, 78–87 (2009)

    Google Scholar 

  16. Skobelev, P.: Multi-agent systems for real time adaptive resource management. In: Leitão, P., Karnouskos, S. (eds.) Industrial Agents: Emerging Applications of Software Agents in Industry, pp. 207–230. Elsevier, Amsterdam (2015)

    Chapter  Google Scholar 

  17. Leitão, P., Colombo, A., Karnouskos, S.: Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges. Comput. Ind. 81, 11–25 (2016)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Smart Solutions, Ltd. - Knowledge Genesis Group, Samara, Russia

    Petr Skobelev

  2. Institute for the Control of Complex Systems of Russian Academy of Sciences, Samara, Russia

    Petr Skobelev

  3. Samara State Technical University, Samara, Russia

    Petr Skobelev

Authors
  1. Petr Skobelev
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Correspondence to Petr Skobelev .

Editor information

Editors and Affiliations

  1. Centre National de la Recherche Scientifique, Grenoble, France

    Yves Demazeau

  2. Nanyang Technological University, Singapore, Singapore

    Bo An

  3. Universidad Politécnica de Madrid, Madrid, Spain

    Javier Bajo

  4. Universidad de Castilla La Mancha, Albacete, Spain

    Antonio Fernández-Caballero

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Skobelev, P. (2018). Towards Autonomous AI Systems for Resource Management: Applications in Industry and Lessons Learned. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Lecture Notes in Computer Science(), vol 10978. Springer, Cham. https://doi.org/10.1007/978-3-319-94580-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-94580-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94579-8

  • Online ISBN: 978-3-319-94580-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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