Abstract
The International Planning Competitions have led to development of a standard modeling framework for describing planning domains and problems – Planning Domain Description Language (PDDL). The majority of planning research is done around problems modeled in PDDL though there are only a few applications adopting PDDL. The planning model of independent actions connected only via causal relations is very flexible, but it also makes plans less predictable (plans look different than expected by the users) and it is probably also one of the reasons of bad practical efficiency of current planners (“visibly” wrong plans are blindly explored by the planners). In this paper we argue that grouping actions into flexible sub-plans is a way to overcome the efficiency problems. The idea is that instead of seeing actions as independent entities that are causally connected via preconditions and effects, we suggest using a form of finite state automaton (FSA) to describe the expected sequences of actions. Arcs in FSA are annotated by conditions guiding the planner to explore only “proper” paths in the automaton. The second idea is composing primitive actions into meta-actions, which decreases the size of a FSA and makes planning much faster. The main motivation is to give users more control over the action sequencing with two primary goals: obtaining more predictable plans and improving efficiency of planning. The presented ideas originate from solving the Petrobras logistic problem, where this technique outperformed classical planning models.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
B-Prolog, http://www.probp.com
International Planning Competitions, http://ipc.icaps-conference.org
Picat, http://www.picat-lang.org
Barták, R.: On Constraint Models for Parallel Planning: The Novel Transition Scheme. In: Proceedings of SCAI 2011. Frontiers of Artificial Intelligence, vol. 227, pp. 50–59. IOS Press (2011)
Botea, A., Enzenberger, M., Muller, M., Schaeffer, J.: Macro-FF: Improving AI planning with automatically learned macro-operators. Journal of Artificial Intelligence Research 24, 581–621 (2005)
Chaslot, G., Bakkes, S., Szita, I., Spronck, P.: Monte-Carlo tree search: A new framework for game AI. In: Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference. The AAAI Press (2008)
Doherty, P., Kvarnström, J.: TALplanner: An empirical investigation of a temporal logic-based forward chaining planner. In: Proceedings of the Sixth International Workshop on Temporal Representation and Reasoning (TIME 1999), pp. 47–54. IEEE Computer Society Press, Orlando (1999)
Dvořák, F., Barták, R.: Integrating time and resources into planning. In: Proceedings of ICTAI 2010, vol. 2, pp. 71–78. IEEE Computer Society (2010)
Erol, K., Hendler, J., Nau, D.: HTN Planning: Complexity and Expressivity. In: Proceedings of AAAI 1994, pp. 1123–1128 (1994)
Gerevini, A., Long, D.: BNF description of PDDL 3.0 (2005), http://cs-www.cs.yale.edu/homes/dvm/papers/pddl-bnf.pdf
Hsu, C., Wah, B.W.: The SGPlan planning system in IPC-6 (2008), http://wah.cse.cuhk.edu.hk/wah/Wah/papers/C168/C168.pdf
Kovacs, D.L.: BNF definition of PDDL 3.1 (2011), http://www.plg.inf.uc3m.es/ipc2011-deterministic/OtherContributions?action=AttachFile&do=view&target=kovacs-pddl-3.1-2011.pdf
Muscettola, N.: HSTS: Integrating Planning and Scheduling. In: Intelligent Scheduling. Morgan Kaufmann (1994)
Toropila, D., Dvořák, F., Trunda, O., Hanes, M., Barták, R.: Three Approaches to Solve the Petrobras Challenge: Exploiting Planning Techniques for Solving Real-Life Logistics Problems. In: Proceedings of ICTAI 2012, pp. 191–198. IEEE Conference Publishing Services (2012)
Vaquero, T.S., Costa, G., Tonidandel, F., Igreja, H., Silva, J.R., Beck, C.: Planning and scheduling ship operations on petroleum ports and platform. In: Proceedings of the Scheduling and Planning Applications Workshop, pp. 8–16 (2012)
Warren, D.S.: Memoing for Logic Programs. CACM 35(3), 93–111 (1992)
Zhou, N.-F., Dovier, A.: A Tabled Prolog Program for Solving Sokoban. In: Proceedings of the 26th Italian Conference on Computational Logic (CILC 2011), pp. 215–228 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barták, R., Zhou, NF. (2013). On Modeling Planning Problems: Experience from the Petrobras Challenge. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-45111-9_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45110-2
Online ISBN: 978-3-642-45111-9
eBook Packages: Computer ScienceComputer Science (R0)