Hostname: page-component-6bf8c574d5-w79xw Total loading time: 0 Render date: 2025-02-16T21:20:48.974Z Has data issue: false hasContentIssue false
  • English
  • Français

Revisiting dynamic constraint satisfaction for model-based planning

Published online by Cambridge University Press:  20 December 2016

Jeremy Frank
Jeremy Frank*
Affiliation:
NASA Ames Research Center, Moffett Field, CA, USA e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

As planning problems become more complex, it is increasingly useful to integrate complex constraints on time and resources into planning models, and use constraint reasoning approaches to help solve the resulting problems. Dynamic constraint satisfaction is a key enabler of automated planning in the presence of such constraints. In this paper, we identify some limitations with the previously developed theories of dynamic constraint satisfaction. We identify a minimum set of elementary transformations from which all other transformations can be constructed. We propose a new classification of dynamic constraint satisfaction transformations based on a formal criteria, namely the change in the fraction of solutions. This criteria can be used to evaluate elementary transformations of a constraint satisfaction problem as well as sequences of transformations. We extend the notion of transformations to include constrained optimization problems. We discuss how this new framework can inform the evolution of planning models, automated planning algorithms, and mixed-initiative planning.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Special Issue 5: Constraint Satisfaction for Planning and Scheduling , November 2016 , pp. 429 - 439
Copyright
© Cambridge University Press, 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Banerjee, D. 2009. Integrating planning and scheduling in a CP framework: a transition-based approach. In Proceedings of the 19 th International Conference on Automated Planning and Scheduling, 330–333.Google Scholar
Bistarelli, S., Montanari, U. & Rossi, F. 1995. Constraint solving over semirings. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, 624–630.Google Scholar
Bresina, J., Jónsson, A., Morris, P. & Rajan, K. 2005. Activity planning for the Mars exploration rovers. In Proceedings of the 15th International Conference on Automated Planning and Scheduling, 40–49.Google Scholar
Dechter, R. & Dechter, A. 1988. Belief maintenance in dynamic constraint networks. In Proceedings of the 7th National Conference on Artificial Intelligence, 37–42.Google Scholar
Do, M. & Kambhampati, S. 2000. Solving planning-graph by compiling it into CSP. In Proceedings of the 5th International Conference on Artificial Intelligence Planning Systems, 82–91.Google Scholar
Frank, J. 2014. Revisiting dynamic constraint satisfaction for automated planning. In Workshop on Constraints and Planning Systems, in conjunction with the 24th International Conference on Automated Planning.Google Scholar
Frank, J. & Jónsson, A. 2003. Constraint based attribute and interval planning. Journal of Constraints Special Issue on Constraints and Planning 8, 339–364.Google Scholar
Frank, J., Jónsson, A. & Morris, P. 2000. On reformulating planning as dynamic constraint satisfaction. In Proceedings of the 4th Symposium on Abstraction, Reformulation and Approximation.Google Scholar
Freuder, E. & Wallace, R. 1992. Partial constraint satisfaction. Artificial Intelligence 58, 21–70.Google Scholar
Ghallab, M. & Laurelle, H. 1994. Representation and control in IxTeT, a temporal planner. In Proceedings of the 4th International Conference on AI Planning and Scheduling, 61–67.Google Scholar
Jónsson, A. & Frank, J. 2000. A framework for dynamic constraint reasoning using procedural constraints. In Proceedings of the 10th European Conference on Artificial Intelligence, 93–97.Google Scholar
Kambhampati, S. 2007. Model-lite planning for the web-age masses: the challenges of planning with incomplete and evolving domain models. In Proceedings of the 13th National Conference on Artificial Intelligence, 1601–1604.Google Scholar
Keyder, E. & Geffner, H. 2008. Heuristics for planning with action costs, revisited. In Proceedings of the 18th European Conference on Artificial Intelligence, 140–149.Google Scholar
Laborie, P. 2003. Algorithms for propagating resource constraints in AI planning and scheduling: existing approaches and new results. Artificial Intelligence 143, 151–188.Google Scholar
Mittal, S. & Falkenhainer, B. 1990. Dynamic constraint satisfaction problems. In Proceedings of the 9th National Conference on Artificial Intelligence, 25–32.Google Scholar
Soininen, T., Gelle, E. & Niemela, I. 1999. A fixpoint definition of dynamic constraint satisfaction. In Proceedings of the 5th International Conference on the Principles and Practices of Constraint Programming, 419–433.Google Scholar
Tsamardinos, I. & Pollack, M. 2003. Efficient solution techniques for disjunctive temporal reasoning problems. Artificial Intelligence 151(1–2), 43–90.Google Scholar
van den Briel, M., Vossen, T. & Kambhampati, S. 2005. Reviving integer programming for AI planning: a branch and cut framework. Proceedings of the 15th International Conference on Automated Planning and Scheduling, 562–569.Google Scholar
van den Briel, M., Sanchez Nigenda, R., Do, M. & Kambhampati, S. 2004. Effective approaches for partial satisfaction (oversubscription) planning. In Proceedings of the 19th National Conference on Artificial Intelligence.Google Scholar
Vaquero, T., Romero, V., Tonidanel, F. & Silva, J. 2007. ItSimple 2.0: an integrated tool for designing planning domains. In Proceedings of the 17th International Conference on Automated Planning and Scheduling, 336–343.Google Scholar
Vidal, V. & Geffner, H. 2006. Branching and pruning: an optimal POCL planner based on constraint programming. Artificial Intelligence 170(3), 298–335.Google Scholar
Wallace, R. 1996. Enhancement of branch and bound methods for the maximal constraint satisfaction problem. In Proceedings of the 13 th National Conference on Artificial Intelligence, 188–195.Google Scholar