Skip to main content
SpringerLink
Log in

Planning with Numeric Key Performance Indicators over Dynamic Organizations of Intelligent Agents

  • Conference paper
Multiagent System Technologies (MATES 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8732))

Included in the following conference series:

  • 858 Accesses

Abstract

In this paper we present a PDDL-based multi-agent planning system for reasoning about key performance indicators (KPIs) in an industrial production planning and control application scenario. On top of PDDL, numeric key figures and associated objectives are configured by the user at run-time and then processed automatically by the system in order to maximize overall goal satisfaction. The organizational structure of the system is a hierarchical multi-agent planning and simulation environment, with KPI objectives being propagated top-down and achievements being assessed bottom-up. KPIs can be automatically aggregated over dynamic groups of agents, with the ability of deliberately planning for reorganization. The planner supports continuous numeric action parameters, which it keeps lifted as sets of intervals before grounding them in delayed fashion with a mathematical optimizer. Plan generation and execution are interleaved. A case study with a simulated shop-floor demonstrates the basic practicability of the approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: JADE: A software framework for developing multi-agent applications. Lessons learned. Inform. Software Tech. 50(1-2), 10–21 (2008)

    Article  Google Scholar 

  2. Bogomolov, S., Magazzeni, D., Podelski, A., Wehrle, M.: Planning as model checking in hybrid domains. In: 28th Conference on Artificial Intelligence (AAAI) (2014)

    Google Scholar 

  3. Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K.: Designing, implementing and updating performance measurement systems. Int. J. Oper. Prod. Man. 20(7), 754–771 (2000)

    Article  Google Scholar 

  4. Bowersox, D.J., Closs, D.J.: Logistical Management: The Integrated Supply Chain Process. McGraw-Hill, New York (1996)

    Google Scholar 

  5. Brafman, R.I., Domshlak, C.: From one to many: Planning for loosely coupled multi-agent systems. In: 24th International Conference on Automated Planning and Scheduling (ICAPS), pp. 28–35 (2008)

    Google Scholar 

  6. Brönnimann, H., Melquiond, G., Pion, S.: The design of the Boost interval arithmetic library. Theor. Comput. Sci. 351(1), 111–118 (2006)

    Article  MATH  Google Scholar 

  7. Bussmann, S., Jennings, N.R., Wooldridge, M.: Multiagent Systems for Manufacturing Control: A Design Methodology. Springer (2004)

    Google Scholar 

  8. Carbonell, J.G., Blythe, J., Etzioni, O., Gil, Y., Joseph, R., Kahn, D., Knoblock, C., Minton, S., Perez, A., Reilly, S., Veloso, M., Wang, X.: Prodigy 4.0: The manual and tutorial. Tech. Rep. CMU-CS-92-150, Carnegie Mellon University, Computer Science Department, Pittsburgh (1992)

    Google Scholar 

  9. Chow, G., Heaver, T.D., Henriksson, L.E.: Logistics performance: Definition and measurement. Int. J. Phys. Distrib. Logist. Manag. 1(24), 17–28 (1994)

    Article  Google Scholar 

  10. Claßen, J., Röger, G., Lakemeyer, G., Nebel, B.: Platas—Integrating planning and the action language Golog. KI 26(1), 61–67 (2012)

    Google Scholar 

  11. Coles, A.J., Coles, A., Fox, M., Long, D.: COLIN: Planning with continuous linear numeric change. J. Artif. Intell. Res. (JAIR) 44, 1–96 (2012)

    Google Scholar 

  12. Dearden, R., Boutilier, C.: Integrating planning and execution in stochastic domains. CoRR abs/1302.6799 (2013), http://arxiv.org/abs/1302.6799

  13. Edelkamp, S.: First solutions to PDDL+ planning problems. In: Workshop of the UK Planning and Scheduling Special Interest Group (PlanSig), pp. 75–88 (2001)

    Google Scholar 

  14. Edelkamp, S., Frank, J., Kellershoff, M.: Knowledge engineering through simulation. In: ICAPS-Workshop on the International Knowledge Engineering Competition (2007)

    Google Scholar 

  15. Ehrgott, M.: Multicriteria Optimization, 2nd edn. Springer (2005)

    Google Scholar 

  16. Fine, N.J.: The jeep problem. Amer. Math. Monthly 54(1), 24–31 (1947)

    Article  MathSciNet  MATH  Google Scholar 

  17. Foundation for Intelligent Physical Agents: FIPA Contract Net interaction protocol specification (2002), http://www.fipa.org/specs/fipa00029

  18. Fox, M., Long, D.: PDDL2.1: An extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. (JAIR) 20, 61–124 (2003)

    Google Scholar 

  19. Franceschini, F., Galetto, M., Maisano, D.: Management by Measurement: Designing Key Indicators and Performance Measurement Systems. Springer (2007)

    Google Scholar 

  20. Frehse, G., Le Guernic, C., Donzé, A., Cotton, S., Ray, R., Lebeltel, O., Ripado, R., Girard, A., Dang, T., Maler, O.: SpaceEx: Scalable verification of hybrid systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 379–395. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  21. Ghallab, M., Nau, D.S., Traverso, P.: Automated Planning: Theory and Practice. Elsevier (2004)

    Google Scholar 

  22. Griewank, A.: On automatic differentiation. In: Iri, M., Tanabe, K. (eds.) Mathematical Programming: Recent Developments and Applications, pp. 83–108. Kluwer (1989)

    Google Scholar 

  23. Henzinger, T.A., Kopke, P.W., Puri, A., Varaiya, P.: What’s decidable about hybrid automata? J. Comput. Syst. Sci. 57(1), 94–124 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  24. Howey, R., Long, D., Fox, M.: VAL: Automatic plan validation, continuous effects and mixed initiative planning using PDDL. In: 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 294–301 (2004)

    Google Scholar 

  25. Jiménez, S., Fernández, F., Borrajo, D.: Integrating planning, execution, and learning to improve plan execution. Computational Intelligence 29(1), 1–36 (2013)

    Article  MathSciNet  Google Scholar 

  26. Koehler, J., Hoffmann, J.: On the instantiation of ADL operators involving arbitrary first-order formulas. In: 14th Workshop on New Results in Planning, Scheduling and Design (PUK), Berlin, pp. 74–82 (2000)

    Google Scholar 

  27. Kovacs, D.L.: A multi-agent extension of PDDL3.1. In: ICAPS-Workshop on the International Planning Competition, pp. 19–27 (2012)

    Google Scholar 

  28. Lorenzen, L., Scholz, T., Timm, I.J., Rudzio, H., Woelk, P., Denkena, B., Herzog, O.: Integrated process planning and production control. In: Kirn, S., Herzog, O., Lockemann, P., Spaniol, O. (eds.) Multiagent Engineering: Theory and Application in Enterprises, pp. 91–114. Springer (2006)

    Google Scholar 

  29. Moore, R.E., Kearfott, R.B., Cloud, M.J.: Introduction to Interval Analysis. SIAM, Philadelphia (2009)

    Google Scholar 

  30. Nebel, B.: Compilation schemes: A theoretical tool for assessing the expressive power of planning formalisms. In: Burgard, W., Christaller, T., Cremers, A.B. (eds.) KI 1999. LNCS (LNAI), vol. 1701, pp. 183–194. Springer, Heidelberg (1999)

    Google Scholar 

  31. Nissim, R., Brafman, R.I.: Cost-optimal planning by self-interested agents. In: 27th Conference on Artificial Intelligence (AAAI), pp. 732–738 (2013)

    Google Scholar 

  32. Pantke, F.: Intelligent agent control and coordination with user-configurable key performance indicators. In: Kreowski, H.J., Scholz-Reiter, B., Thoben, K.D. (eds.) Dynamics in Logistics, pp. 145–159. Springer (2011)

    Google Scholar 

  33. Pantke, F., Edelkamp, S., Herzog, O.: Combinatorial planning with numerical parameter optimization for local control in multi-agent systems. In: 2nd International Conference on System-Integrated Intelligence: Challenges for Product and Production Engineering (SysInt). Procedia Technology. Elsevier (2014)

    Google Scholar 

  34. Piacentini, C., Alimisis, V., Fox, M., Long, D.: Combining a temporal planner with an external solver for the power balancing problem in an electricity network. In: 23rd International Conference on Automated Planning and Scheduling (ICAPS), pp. 398–406 (2013)

    Google Scholar 

  35. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer (2005)

    Google Scholar 

  36. Ruml, W., Do, M.B., Zhou, R., Fromherz, M.P.J.: On-line planning and scheduling: An application to controlling modular printers. J. Artif. Intell. Res. (JAIR) 40, 415–468 (2011)

    Google Scholar 

  37. Stolba, M., Komenda, A.: Relaxation heuristics for multiagent planning. In: 24th International Conference on Automated Planning and Scheduling (ICAPS), pp. 298–306 (2014)

    Google Scholar 

  38. Windt, K., Becker, T., Jeken, O., Gelessus, A.: A classification pattern for autonomous control methods in logistics. Logistics Research 2(2), 109–120 (2010)

    Article  Google Scholar 

  39. Zamani, Z., Sanner, S., Fang, C.: Symbolic dynamic programming for continuous state and action MDPs. In: 26th Conference on Artificial Intelligence (AAAI), pp. 1839–1845 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Computing and Communication Technologies (TZI), University of Bremen, Am Fallturm 1, 28359, Bremen, Germany

    Florian Pantke, Stefan Edelkamp & Otthein Herzog

Authors
  1. Florian Pantke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Edelkamp
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Otthein Herzog
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Technische Universität Clausthal, Clausthal-Zellerfeld, Germany

    Jörg P. Müller

  2. institut für Automatisierungs- und Softwaretechnik, Universität Stuttgart, Pfaffenwaldring 47, 70550, Stuttgart, Germany

    Michael Weyrich

  3. Instituto de Informática, Universidade Federal do Rio Grande do Sul (UFRGS), Caixa Postal 15064, 91501-970, Porto Alegre, Rio Grande do Sul, Brazil

    Ana L. C. Bazzan

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Pantke, F., Edelkamp, S., Herzog, O. (2014). Planning with Numeric Key Performance Indicators over Dynamic Organizations of Intelligent Agents. In: Müller, J.P., Weyrich, M., Bazzan, A.L.C. (eds) Multiagent System Technologies. MATES 2014. Lecture Notes in Computer Science(), vol 8732. Springer, Cham. https://doi.org/10.1007/978-3-319-11584-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11584-9_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11583-2

  • Online ISBN: 978-3-319-11584-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation