Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Answer Set Programming for Modeling and Reasoning on Modular and Reconfigurable Transportation Systems

, ,

DOI: http://dx.doi.org/10.15439/2017F154

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 587596 ()

Full text

Abstract. This paper addresses the modeling of modular and reconfigurable transportation systems, aiming at developing tools to support the planning and control. Answer Set Programming (ASP) is employed to formalize rules modeling the characteristic of a transportation system and describing its dynamics. Then, automatic reasoning can be exploited to find solutions in different use cases, including the generation of optimal or alternative paths, the generation and validation of control sequences. The proposed methodology is applied to a reconfigurable industrial transportation system consisting of multiple linear conveyor modules with actuators enabling longitudinal and transversal movements of pallets.


  1. Y. Koren, U. Heisel, F. Jovane, T. Moriwaki, G. Pritschow, G. Ulsoy, and H. V. Brussel, “Reconfigurable manufacturing systems,” CIRP Annals - Manufacturing Technology, vol. 48, no. 2, pp. 527–540, 1999.
  2. A. Gola and A. Swic, “Reconfigurable manufacturing systems as a way of long-term economic capacity management,” Actual Problems of Economics, vol. 166, no. 4, pp. 15–22, 2015. cited By 0.
  3. E. Carpanzano, A. Cesta, A. Orlandini, R. Rasconi, and A. Valente, “Intelligent dynamic part routing policies in plug&produce reconfigurable transportation systems,” CIRP Annals - Manufacturing Technology, vol. 63, no. 1, pp. 425–428, 2014.
  4. S. Haneyah, J. Schutten, P. Schuur, and W. Zijm, “Generic planning and control of automated material handling systems: Practical requirements versus existing theory,” Computers in Industry, vol. 64, no. 3, pp. 177–190, 2013.
  5. A. Cataldo and R. Scattolini, “Modeling and model predictive control of a de-manufacturing plant,” in 2014 IEEE Conference on Control Applications (CCA), pp. 1855–1860, Oct 2014.
  6. I. Hegny, O. Hummer, A. Zoitl, G. Koppensteiner, and M. Merdan, “Integrating software agents and iec 61499 realtime control for reconfigurable distributed manufacturing systems,” in 2008 International Symposium on Industrial Embedded Systems, pp. 249–252, June 2008.
  7. C. A. Petri, Kommunikation mit Automaten. PhD thesis, Universität Hamburg, 1962.
  8. M. Gelfond and V. Lifschitz, “The stable model semantics for logic programming,” in Proceedings of International Logic Programming Conference and Symposium (R. Kowalski, Bowen, and Kenneth, eds.), pp. 1070–1080, MIT Press, 1988.
  9. V. Lifschitz, “What is answer set programming?,” in Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 3, AAAI’08, pp. 1594–1597, AAAI Press, 2008.
  10. G. Brewka, T. Eiter, and M. Truszczyński, “Answer set programming at a glance,” Commun. ACM, vol. 54, pp. 92–103, Dec. 2011.
  11. V. Lifschitz, “Answer set programming and plan generation,” Artificial Intelligence, vol. 138, no. 1, pp. 39 – 54, 2002.
  12. E. Aker, V. Patoglu, and E. Erdem, “Answer set programming for reasoning with semantic knowledge in collaborative housekeeping robotics,” IFAC Proceedings Volumes, vol. 45, no. 22, pp. 77 – 83, 2012.
  13. J. J. Portillo, C. L. Garcia-Mata, P. R. Márquez-Gutiérrez, and R. Baray-Arana, “Robot platform motion planning using answer set programming,” in LA-NMR, 2011.
  14. F. Yang, P. Khandelwal, M. Leonetti, and P. Stone, “Planning in answer set programming while learning action costs for mobile robots,” in AAAI Spring 2014 Symposium on Knowledge Representation and Reasoning in Robotics (AAAI-SSS), March 2014.
  15. S. Anwar, C. Baral, and K. Inoue, “Encoding petri nets in answer set programming for simulation based reasoning,” CoRR, vol. abs/1306.3542, 2013.
  16. S. Anwar, C. Baral, and K. Inoue, Encoding Higher Level Extensions of Petri Nets in Answer Set Programming, pp. 116–121. Springer Berlin Heidelberg, 2013.
  17. M. Gebser, R. Kaminski, B. Kaufmann, M. Lindauer, M. Ostrowski, J. Romero, T. Schaub, and S. Thiele, “Potassco User Guide,” 2015.
  18. A. Cataldo, R. Scattolini, and T. Tolio, “An energy consumption evaluation methodology for a manufacturing plant,” {CIRP} Journal of Manufacturing Science and Technology, vol. 11, pp. 53 – 61, 2015.
  19. M. R. Blackburn and P. O. Denno, “Using semantic web technologies for integrating domain specific modeling and analytical tools,” Procedia Computer Science, vol. 61, pp. 141 – 146, 2015. Complex Adaptive Systems San Jose, CA November 2-4, 2015.
  20. W. Terkaj, T. Tolio, and M. Urgo, “A virtual factory approach for in situ simulation to support production and maintenance planning,” {CIRP} Annals - Manufacturing Technology, vol. 64, no. 1, pp. 451–454, 2015.