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Constraint-based modeling of discrete event dynamic systems

Abstract

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.

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References

  • Alur R., Dill D. (1994) A theory of timed automata. Journal of Theoretical Computer Science 126(2): 183–235

    Article  Google Scholar 

  • Arnold, A., & Nivat, M. (1982). Comportements de processus. In Actes du Colloque AFCET “Les MathTmatiques de l’Informatique” (pp. 35–68). Paris.

  • Baptiste, P., Pape, C. L., & Nuijten, W. (2001). Constraint-based scheduling: Applying constraint programming to scheduling problems. Kluwer Academic Publishers.

  • Barták, R. (1999). Dynamic constraint models for complex production environments. In Proceedings of the Joint ERCIM/Compulog-Net Workshop. Cyprus.

  • 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): 64–83

    Article  Google Scholar 

  • Clarke E., Biere A., Raimi R., Zhu Y. (2001) Bounded model checking using satisfiability solving. Formal Methods in System Design, 19(1): 7–34

    Article  Google Scholar 

  • Dechter R. (1999) Bucket elimination: a unifying framework for reasoning. Artificial Intelligence 113: 41–85

    Article  Google Scholar 

  • Dechter, R. (2003). Constraint Processing. Morgan Kaufmann.

  • 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.

  • Dechter R., Meiry I., Pearl J. (1991) Temporal constraint networks. Artificial Intelligence 49: 61–95

    Article  Google Scholar 

  • Fikes R., Nilsson N. (1971) STRIPS: a new approach to the application of theorem proving. Artificial Intelligence 2: 189–208

    Article  Google Scholar 

  • Fox M., Long D. (2003) PDDL2.1 : An extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20: 61–124

    Google Scholar 

  • Frank J., Jónsson A. (2003) Constraint-based attribute and interval planning. Constraints 8(4): 339–364

    Article  Google Scholar 

  • Gelle E., Faltings B. (2003) Solving mixed and conditional constraint satisfaction problems. Constraints 8(2): 107–141

    Article  Google Scholar 

  • 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.

  • Ghallab, M., Nau, D., & Traverso, P. (2004). Automated Planning: Theory and Practice. Morgan Kaufmann.

  • 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.

  • 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.

  • Kowalski R., Sergot M. (1986) A Logic-based calculus of events. New Generation Computing 4: 67–95

    Article  Google Scholar 

  • Kushmerick N., Hanks S., Weld D. (1995) An algorithm for probabilistic planning. Artificial Intelligence 76: 239–286

    Article  Google Scholar 

  • 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.

  • 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): 59–83

    Article  Google Scholar 

  • McDermott, D. (1998). PDDL—the planning domain definition language.

  • Miguel I., Shen Q., Jarvis P. (2001) Efficient flexible planning via dynamic flexible constraint satisfaction. Engineering Applications of Artificial Intelligence 14(3): 301–327

    Article  Google Scholar 

  • 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.

  • Muscettola N. (1994). HSTS: integrating planning and scheduling. In M. Zweden, & M. Fox (Eds.), Intelligent Scheduling (pp. 169–212). Morgan Kaufmann.

  • 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): 5–48

    Article  Google Scholar 

  • 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.

  • Nareyek, A., Freuder, E., Fourer, R., Giunchiglia, E., Goldman, R., Kautz, H., Rintanen, J., & Tate, A. (2005). Constraints and AI Planning. IEEE Intelligent Systems.

  • 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.

  • 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.

  • Pralet C., Verfaillie G., Schiex T. (2007) An algebraic graphical model for decision with uncertainties, feasibilities, and utilities. Journal of Artificial Intelligence Research 29: 421–489

    Google Scholar 

  • Puterman, M. (1994). Markov Decision Processes, Discrete Stochastic Dynamic Programming. Wiley.

  • Rossi, R., Beek, P. V., & Walsh, T. (Eds.). (2006). Handbook of Constraint Programming. Elsevier.

  • 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.

  • 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.

  • 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.

  • 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.

  • Verfaillie G., Jussien N. (2005) Constraint solving in uncertain and dynamic environments: A survey. Constraints 10(3): 253–281

    Article  Google Scholar 

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Correspondence to Gérard Verfaillie.

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Verfaillie, G., Pralet, C. & Lemaître, M. Constraint-based modeling of discrete event dynamic systems. J Intell Manuf 21, 31–47 (2010). https://doi.org/10.1007/s10845-008-0176-3

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  • DOI: https://doi.org/10.1007/s10845-008-0176-3

Keywords

  • Discrete event dynamic systems
  • Constraint-based modeling
  • Timelines