A Unifying Framework for Hybrid Planning and Scheduling
Many real-world application domains that demand planning and scheduling support do not allow for a clear separation of these capabilities. Typically, an adequate mixture of both methodologies is required, since some aspects of the underlying planning problem imply consequences on the scheduling part and vice versa. Several integration efforts have been undertaken to couple planning and scheduling methods, most of them using separate planning and scheduling components which iteratively exchange partial solutions until both agree on a result.
This paper presents a framework that provides a uniform integration of hybrid planning –the combination of operator based partial order planning and abstraction based hierarchical task network planning– and a hierarchical scheduling approach. It is based on a proper formal account of refinement planning, which allows for the formal definition of hybrid planning, scheduling, and search strategies. In a first step, the scheduling functionality is used to produce plans that comply with time restrictions and resource bounds. We show how the resulting framework is thereby able to perform novel kinds of search strategies that opportunistically interleave what used to be separate planning and scheduling processes.
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- 2.Biundo, S., Schattenberg, B.: From abstract crisis to concrete relief – A preliminary report on combining state abstraction and HTN planning. In: Cesta, A., Borrajo, D. (eds.) Proceedings of the 6th European Conference on Planning, pp. 157–168. Springer, Heidelberg (2001)Google Scholar
- 3.Castillo, L., Fdez-Olivares, J., González, A.: On the adequacy of hierarchical planning characteristics for real-world problem solving. In: Proceedings of the 6th European Conference on Planning (2001)Google Scholar
- 4.Drabble, B., Tate, A.: The use of optimistic and pessimistic resource profiles to inform search in an activity based planner. In: Hammond, K. (ed.) Proceedings of the 2nd International Conference on Artificial Intelligence Planning Systems, pp. 243–248. AAAI Press, Menlo Park (1994)Google Scholar
- 5.Garrido, A., Salido, M.A., Barber, F.: Scheduling in a planning environment. In: Sauer, J., Köhler, J. (eds.) Proceedings of the 14th European Conference on Artificial Intelligence Workshop on New Results in Planning, Scheduling and Design, pp. 36–43 (2000)Google Scholar
- 7.El-Kholy, A., Richards, B.: Temporal and resource reasoning in planning: The parcPLAN approach. In: Wahlster, W. (ed.) Proceedings of the 12th European Conference on Artificial Intelligence, pp. 614–618. John Wiley & Sons, Chichester (1996)Google Scholar
- 8.Laborie, P., Ghallab, M.: Planning with sharable resource constraints. In: Mellish, C.S. (ed.) Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 1643–1651. Morgan Kaufmann, San Francisco (1995)Google Scholar
- 9.Schattenberg, B., Biundo, S.: On the identification and use of hierarchical resources in planning and scheduling. In: Ghallab, M., Hertzberg, J., Traverso, P. (eds.) Proceedings of the 6th International Conference on Artificial Intelligence Planning Systems, pp. 263–272. AAAI Press, Menlo Park (2002)Google Scholar
- 10.Garcia, F., Laborie, P.: Hierarchisation of the seach space in temporal planning. In: Ghallab, M., Milani, A. (eds.) New Directions in AI Planning, Proceedings of the 3rd European Workshop on AI Planning. Frontiers in Artificial Intelligence, vol. 31, pp. 217–232. IOS Press, Amsterdam (1996)Google Scholar
- 12.Clement, B.J., et al.: Using abstraction in planning and scheduling. In: Cesta, A., Borrajo, D. (eds.) Proceedings of the 6th European Conference on Planning, pp. 145–156. Springer, Heidelberg (2001)Google Scholar
- 14.Lhomme, O.: Consistency techniques for numeric CSPs. In: Bajcsy, R. (ed.) Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 232–238. Morgan Kaufmann, San Francisco (1993)Google Scholar
- 15.Biundo, S., Holzer, R., Schattenberg, B.: Dealing with continuous resources in AI planning. In: Proceedings of the 4th International Workshop on Planning and Scheduling for Space (IWPSS’04), pp. 213–218. European Space Agency Publications Division (2004)Google Scholar
- 16.Biundo, S., Holzer, R., Schattenberg, B.: Project planning under temporal uncertainty. In: Castillo, L., et al. (eds.) Planning, Scheduling, and Constraint Satisfaction: From Theory to Practice. Frontiers in Artificial Intelligence and Applications, vol. 117, pp. 189–198. IOS Press, Amsterdam (2005)Google Scholar