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How to model planning and scheduling problems using constraint networks on timelines

Published online by Cambridge University Press:  01 September 2010

Gérard Verfaillie*
Affiliation:
ONERA, 2 av. Édouard Belin, BP 74025, F-31055 Toulouse Cedex 4, France
Cédric Pralet*
Affiliation:
ONERA, 2 av. Édouard Belin, BP 74025, F-31055 Toulouse Cedex 4, France
Michel Lemaître*
Affiliation:
ONERA, 2 av. Édouard Belin, BP 74025, F-31055 Toulouse Cedex 4, France

Abstract

The CNT framework (Constraint Network on Timelines) has been designed to model discrete event dynamic systems and the properties one knows, one wants to verify, or one wants to enforce on them. In this article, after a reminder about the CNT framework, we show its modeling power and its ability to support various modeling styles, coming from the planning, scheduling, and constraint programming communities. We do that by producing and comparing various models of two mission management problems in the aerospace domain: management of a team of unmanned air vehicles and of an Earth observing satellite.

Type
Articles
Copyright
Copyright © Cambridge University Press 2010

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References

Baptiste, P., Pape, C.L., Nuijten, W. 2001. Constraint-based Scheduling: Applying Constraint Programming to Scheduling Problems. Kluwer Academic Publishers.CrossRefGoogle Scholar
Barták, R. 2002. Visopt ShopFloor: on the edge of planning and scheduling. Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP), Lecture Notes in Computer Science 2470, 587602.Google Scholar
Benveniste, A., Caspi, P., Edwards, P., Halbwachs, N., Guernic, P.L., de Simone, R. 2003. The synchronous languages twelve years later. Proceedings of the IEEE 91(1), 6483.CrossRefGoogle Scholar
Cesta, A., Oddi, A. 1996. DDL.1: a formal description of a constraint representation language for physical domains. In New Directions in AI Planning, Ghallab, M. & Milani A. (eds). IOS Press.Google Scholar
Do, M., Kambhampati, S. 2001. Planning as constraint satisfaction: solving the planning-graph by compiling it into CSP. Artificial Intelligence 132(2), 151182.CrossRefGoogle Scholar
Fox, M., Long, D. 2003. PDDL2.1: an extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61124.CrossRefGoogle Scholar
Frank, J., Jónsson, A. 2003. Constraint-based attribute and interval planning. Constraints 8(4), 339364.CrossRefGoogle Scholar
Fratini, S., Pecora, F., Cesta, A. 2008. Unifying planning and scheduling as timelines in a component-based perspective. Archives of Control Sciences 18(2), 545.Google Scholar
Ghallab, M., Laruelle, H. 1994. Representation and control in IxTeT: a temporal planner. In Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling (AIPS), AAAI Press, 6167.Google Scholar
Ghallab, M., Nau, D., Traverso, P. 2004. Automated Planning: Theory and Practice. Morgan Kaufmann.Google Scholar
Jónsson, A., Morris, P., Muscettola, N., Rajan, K., Smith, B. 2000. Planning in interplanetary space: theory and practice. In Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling (AIPS), AAAI Press, 177186.Google Scholar
Kautz, H., Selman, B. 1992. Planning as satisfiability. In Proceedings of the European Conference on Artificial Intelligence (ECAI), John Wiley & Sons, 359363.Google Scholar
Mittal, S., Falkenhainer, B. 1990. Dynamic constraint satisfaction problems. In Proceedings of the National Conference on Artificial Intelligence (AAAI), AAAI Press, 2532.Google Scholar
Muscettola, N. 1994. HSTS: integrating planning and scheduling. In Intelligent Scheduling, Zweden, M. & Fox, M. (eds). Morgan Kaufmann, 169212.Google Scholar
Nareyek, A., Freuder, E., Fourer, R., Giunchiglia, E., Goldman, R., Kautz, H., Rintanen, J., Tate, A. 2005. Constraints and AI planning. IEEE Intelligent Systems 20(2), 6272.CrossRefGoogle Scholar
Pralet, C., Verfaillie, G. 2008a. Decision upon observations and data downloads by an autonomous earth surveillance satellite. In Proceedings of the International Symposium on Artificial Intelligence, Robotics, and Automation for Space (iSAIRAS), AAAI Press.Google Scholar
Pralet, C., Verfaillie, G. 2008b. Using constraint networks on timelines to model and solve planning and scheduling problems. In Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling (ICAPS), 272279.Google Scholar
Pralet, C., Verfaillie, G., Schiex, T. 2007. An algebraic graphical model for decision with uncertainties, feasibilities, and utilities. Journal of Artificial Intelligence Research 29, 421489.CrossRefGoogle Scholar
Puterman, M. 1994. Markov Decision Processes, Discrete Stochastic Dynamic Programming. John Wiley & Sons.CrossRefGoogle Scholar
Rossi, R., Beek, P.V., Walsh, T. (eds) 2006. Handbook of Constraint Programming. Elsevier.Google Scholar
Schiex, T., Fargier, H., Verfaillie, G. 1995. Valued constraint satisfaction problems: hard and easy problems. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Morgan Kaufman, 631–637.Google Scholar
Smith, D., Frank, J., Cushing, W. 2008. The ANML Language. In Proceedings of the International Conference on Artificial Intelligence Planning and Scheduling (ICAPS), AAAI Press.Google Scholar
van Beek, P., Chen, X. 1999. CPlan: a constraint programming approach to planning. In Proceedings of the National Conference on Artificial Intelligence (AAAI), AAAI Press, 585590.Google Scholar
Verfaillie, G., Pralet, C., Lemaître, M. 2008. Constraint-based modeling of discrete event dynamic systems. Journal of Intelligent Manufacturing, Special Issue on “Planning, Scheduling, and Constraint Satisfaction”. Published online.Google Scholar
Vidal, V., Geffner, H. 2006. Branching and pruning: an optimal temporal POCL planner based on constraint programming. Artificial Intelligence 170, 298335.CrossRefGoogle Scholar