Journal of Intelligent Manufacturing

, Volume 21, Issue 1, pp 31–47 | Cite as

Constraint-based modeling of discrete event dynamic systems

  • Gérard Verfaillie
  • Cédric Pralet
  • Michel Lemaître


Numerous frameworks dedicated to the modeling of discrete event dynamic systems have been proposed to deal with programming, simulation, validation, situation tracking, or decision tasks: automata, Petri nets, Markov chains, synchronous languages, temporal logics, event and situation calculi, STRIPS…All these frameworks present significant similarities, but none offers the flexibility of more generic frameworks such as logic or constraints. In this article, we propose a generic constraint-based framework for the modeling of discrete event dynamic systems, whose basic components are state, event, and time attributes, as well as constraints on these attributes, and which we refer to as CNT for Constraint Network on Timelines. The main strength of such a framework is that it allows any kind of constraint to be defined on state, event, and time attributes. Moreover, its great flexibility allows it to subsume existing apparently different frameworks such as automata, timed automata, Petri nets, and classical frameworks used in planning and scheduling.


Discrete event dynamic systems Constraint-based modeling Timelines 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alur R., Dill D. (1994) A theory of timed automata. Journal of Theoretical Computer Science 126(2): 183–235CrossRefGoogle Scholar
  2. Arnold, A., & Nivat, M. (1982). Comportements de processus. In Actes du Colloque AFCET “Les MathTmatiques de l’Informatique” (pp. 35–68). Paris.Google Scholar
  3. Baptiste, P., Pape, C. L., & Nuijten, W. (2001). Constraint-based scheduling: Applying constraint programming to scheduling problems. Kluwer Academic Publishers.Google Scholar
  4. Barták, R. (1999). Dynamic constraint models for complex production environments. In Proceedings of the Joint ERCIM/Compulog-Net Workshop. Cyprus.Google Scholar
  5. Benveniste A., Caspi P., Edwards S., Halbwachs N., Guernic P.L., de Simone R. (2003) The synchronous languages twelve years later. Proceedings of the IEEE 91(1): 64–83CrossRefGoogle Scholar
  6. Clarke E., Biere A., Raimi R., Zhu Y. (2001) Bounded model checking using satisfiability solving. Formal Methods in System Design, 19(1): 7–34CrossRefGoogle Scholar
  7. Dechter R. (1999) Bucket elimination: a unifying framework for reasoning. Artificial Intelligence 113: 41–85CrossRefGoogle Scholar
  8. Dechter, R. (2003). Constraint Processing. Morgan Kaufmann.Google Scholar
  9. Dechter, R., & Dechter, A. (1988). Belief maintenance in dynamic constraint networks. In Proceedings of the 7th National Conference on Artificial Intelligence (AAAI-88)(pp. 37–42). St. Paul, MN, USA.Google Scholar
  10. Dechter R., Meiry I., Pearl J. (1991) Temporal constraint networks. Artificial Intelligence 49: 61–95CrossRefGoogle Scholar
  11. Fikes R., Nilsson N. (1971) STRIPS: a new approach to the application of theorem proving. Artificial Intelligence 2: 189–208CrossRefGoogle Scholar
  12. Fox M., Long D. (2003) PDDL2.1 : An extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20: 61–124Google Scholar
  13. Frank J., Jónsson A. (2003) Constraint-based attribute and interval planning. Constraints 8(4): 339–364CrossRefGoogle Scholar
  14. Gelle E., Faltings B. (2003) Solving mixed and conditional constraint satisfaction problems. Constraints 8(2): 107–141CrossRefGoogle Scholar
  15. Ghallab, M. (1996). On chronicles: representation, on-line recognition and learning. In Proceedings of the 5th International Conference on the Principles of Knowledge Representation and Reasoning (KR-96) (pp. 597–606). Boston, MA, USA.Google Scholar
  16. Ghallab, M., Nau, D., & Traverso, P. (2004). Automated Planning: Theory and Practice. Morgan Kaufmann.Google Scholar
  17. Hickmott, S., Rintanen, J., ThiTbaux, S., & White, L. (2007). Planning via petri net unfolding. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07) (pp. 1904–1911). Hyderabad, India.Google Scholar
  18. Kautz, H., & Selman, B. (1992). Planning as satisfiability. In Proceedings of the 10th European Conference on Artificial Intelligence (ECAI-92) (pp. 359–363). Vienna, Austria.Google Scholar
  19. Kowalski R., Sergot M. (1986) A Logic-based calculus of events. New Generation Computing 4: 67–95CrossRefGoogle Scholar
  20. Kushmerick N., Hanks S., Weld D. (1995) An algorithm for probabilistic planning. Artificial Intelligence 76: 239–286CrossRefGoogle Scholar
  21. Laborie, P., Ghallab, M. (1995). IxTeT: an integrated approach for plan generation and scheduling. In Proceedings of the 4th INRIA/IEEE Symposium on Emerging Technologies and Factory Automation (ETFA-95) (pp. 485–495). Paris, France.Google Scholar
  22. Levesque H., Reiter R., Lesperance Y., Lin F., Scherl R. (1997) GOLOG: a logic programming language for dynamic domains. Journal of Logic Programming 31(1–3): 59–83CrossRefGoogle Scholar
  23. McDermott, D. (1998). PDDL—the planning domain definition language.Google Scholar
  24. Miguel I., Shen Q., Jarvis P. (2001) Efficient flexible planning via dynamic flexible constraint satisfaction. Engineering Applications of Artificial Intelligence 14(3): 301–327CrossRefGoogle Scholar
  25. Mittal, S., Falkenhainer, B. (1990). Dynamic constraint satisfaction problems. In Proceedings of the 8th National Conference on Artificial Intelligence (AAAI-90) (pp. 25–32). Boston, MA, USA.Google Scholar
  26. Muscettola N. (1994). HSTS: integrating planning and scheduling. In M. Zweden, & M. Fox (Eds.), Intelligent Scheduling (pp. 169–212). Morgan Kaufmann.Google Scholar
  27. Muscettola N., Nayak P., Pell B., Williams B. (1998) Remote agent: to boldly go where no AI system has gone before. Artificial Intelligence 103(1–2): 5–48CrossRefGoogle Scholar
  28. Nareyek, A. (2001). Constraints-based agents—an architecture for constraint-based modeling and local-search-based reasoning for planning and scheduling in open and dynamic worlds. Springer.Google Scholar
  29. Nareyek, A., Freuder, E., Fourer, R., Giunchiglia, E., Goldman, R., Kautz, H., Rintanen, J., & Tate, A. (2005). Constraints and AI Planning. IEEE Intelligent Systems.Google Scholar
  30. Penberthy, J., & Weld, D. (1994). Temporal planning with continuous change. In Proceedings of the 12th National Conference on Artificial Intelligence (AAAI-94) (pp. 1010–1015). Seattle, WA, USA.Google Scholar
  31. Pnueli, A. (1977). The temporal logic of programs. In Proceedings of the 18th IEEE Symposium on the Foundations of Computer Science (FOCS-77) (pp. 46–57). Providence, RI, USA.Google Scholar
  32. Pralet C., Verfaillie G., Schiex T. (2007) An algebraic graphical model for decision with uncertainties, feasibilities, and utilities. Journal of Artificial Intelligence Research 29: 421–489Google Scholar
  33. Puterman, M. (1994). Markov Decision Processes, Discrete Stochastic Dynamic Programming. Wiley.Google Scholar
  34. Rossi, R., Beek, P. V., & Walsh, T. (Eds.). (2006). Handbook of Constraint Programming. Elsevier.Google Scholar
  35. Sabin, M., Freuder, E., Wallace, R. (2003). Greater efficiency for conditional constraint satisfaction. In Proceedings of the 9th International Conference on Principles and Practice of Constraint Programming (CP-03) (pp. 649–663). Cork, Ireland.Google Scholar
  36. Schiex, T., Fargier, H., Verfaillie, G. (1995). Valued constraint satisfaction problems: Hard and easy problems. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95) (pp. 631–637). Montréal, Canada.Google Scholar
  37. Trinquart, R., & Ghallab, M. (2001). An extended functional representation in temporal plannning: towards continuous change. In Proceedings of the 6th European Conference on Planning (ECP-01). Toledo, Spain.Google Scholar
  38. van Beek, P., & Chen, X. (1999). CPlan: A constraint programming approach to planning. In Proceedings of the 16th National Conference on Artificial Intelligence (AAAI-99) (pp 585–590). Orlando, FL, USA.Google Scholar
  39. Verfaillie G., Jussien N. (2005) Constraint solving in uncertain and dynamic environments: A survey. Constraints 10(3): 253–281CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Gérard Verfaillie
    • 1
  • Cédric Pralet
    • 1
  • Michel Lemaître
    • 1
  1. 1.ONERAToulouse Cédex 4France

Personalised recommendations