Abstract
Domain-independent planning is one of the long-standing sub-areas of Artificial Intelligence (AI), aiming at approaching human problem-solving flexibility. The area has long had an affinity towards playful illustrative examples, imprinting it on the mind of many a student as an area concerned with the rearrangement of blocks, and with the order in which to put on socks and shoes (not to mention the disposal of bombs in toilets). Working on the assumption that this “student” is you – the readers in earlier stages of their careers – I herein aim to answer three questions that you surely desired to ask back then already: What is it good for? Does it work? Is it interesting to do research in? Answering the latter two questions in the affirmative (of course!), I outline some of the major developments of the last decade, revolutionizing the ability of planning to scale up, and the understanding of the enabling technology. Answering the first question, I point out that modern planning proves to be quite useful for solving practical problems - including, perhaps, yours.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. Artificial Intelligence 90(1-2), 279–298 (1997)
Bonet, B., Geffner, H.: Planning as heuristic search. Artificial Intelligence 129(1–2), 5–33 (2001)
Bonet, B., Geffner, H.: Heuristics for planning with penalties and rewards formulated in logic and computed through circuits. Artificial Intelligence 172(12-13), 1579–1604 (2008)
Bonet, B., Helmert, M.: Strengthening landmark heuristics via hitting sets. In: Proceedings of the 19th European Conference on Artificial Intelligence (2010)
Bonet, B., Loerincs, G., Geffner, H.: A robust and fast action selection mechanism for planning. In: Proceedings of the 14th National Conference of the American Association for Artificial Intelligence (1997)
Bylander, T.: The computational complexity of propositional STRIPS planning. Artificial Intelligence 69(1–2), 165–204 (1994)
Castillo, L.A., Morales, L., González-Ferrer, A., Fernández-Olivares, J., Borrajo, D., Onaindia, E.: Automatic generation of temporal planning domains for e-learning problems. Journal of Scheduling 13(4), 347–362 (2010)
Cresswell, S., McCluskey, T.L., West, M.M.: Acquisition of object-centred domain models from planning examples. In: Proceedings of the 19th International Conference on Automated Planning and Scheduling (2009)
Edelkamp, S.: Planning with pattern databases. In: Recent Advances in AI Planning. 6th European Conference on Planning (2001)
Fox, M., Long, D.: PDDL2.1: An extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61–124 (2003)
Gerevini, A., Saetti, A., Serina, I.: Planning through stochastic local search and temporal action graphs. Journal of Artificial Intelligence Research 20, 239–290 (2003)
Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Morgan Kaufmann, San Francisco (2004)
Haslum, P., Geffner, H.: Admissible heuristics for optimal planning. In: Proceedings of the 5th International Conference on Artificial Intelligence Planning Systems (2000)
Helmert, M., Domshlak, C.: Landmarks, critical paths and abstractions: What’s the difference anyway? In: Proceedings of the 19th International Conference on Automated Planning and Scheduling (2009)
Helmert, M., Haslum, P., Hoffmann, J.: Flexible abstraction heuristics for optimal sequential planning. In: Proceedings of the 17th International Conference on Automated Planning and Scheduling (2007)
Hoffmann, J.: Where ignoring delete lists works, part II: Causal graphs. In: Proceedings of the 21st International Conference on Automated Planning and Scheduling (2011)
Hoffmann, J.: Local search topology in planning benchmarks: An empirical analysis. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (2001)
Hoffmann, J.: Local search topology in planning benchmarks: A theoretical analysis. In: Proceedings of the 6th International Conference on Artificial Intelligence Planning and Scheduling (2002)
Hoffmann, J.: The Metric-FF planning system: Translating “ignoring delete lists” to numeric state variables. Journal of Artificial Intelligence Research 20, 291–341 (2003)
Hoffmann, J.: Utilizing Problem Structure in Planning: A Local Search Approach. LNCS (LNAI), vol. 2854. Springer, Heidelberg (2003)
Hoffmann, J.: Where ‘ignoring delete lists’ works: Local search topology in planning benchmarks. Journal of Artificial Intelligence Research 24, 685–758 (2005)
Hoffmann, J.: Analyzing search topology without running any search: On the connection between causal graphs and h + . Journal of Artificial Intelligence Research 41, 155–229 (2011)
Hoffmann, J., Nebel, B.: The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302 (2001)
Hoffmann, J., Weber, I., Kraft, F.M.: SAP speaks PDDL. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (2010)
Karpas, E., Domshlak, C.: Cost-optimal planning with landmarks. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (2009)
Katz, M., Domshlak, C.: Optimal additive composition of abstraction-based admissible heuristics. In: Proceedings of the 18th International Conference on Automated Planning and Scheduling (2008)
Koller, A., Hoffmann, J.: Waking up a sleeping rabbit: On natural-language sentence generation with FF. In: Proceedings of the 20th International Conference on Automated Planning and Scheduling (2010)
Koller, A., Petrick, R.: Experiences with planning for natural language generation. Computational Intelligence 27(1), 23–40 (2011)
Lucangeli, J., Sarraute, C., Richarte, G.: Attack planning in the real world. In: Proceedings of the 2nd Workshop on Intelligent Security (2010)
McDermott, D.: A heuristic estimator for means-ends analysis in planning. In: Proceedings of the 3rd International Conference on Artificial Intelligence Planning Systems (1996)
McDermott, D., et al.: The PDDL Planning Domain Definition Language. In: The AIPS 1998 Planning Competition Committee (1998)
Richter, S., Helmert, M.: Preferred operators and deferred evaluation in satisficing planning. In: Proceedings of the 19th International Conference on Automated Planning and Scheduling (2009)
Richter, S., Westphal, M.: The LAMA planner: Guiding cost-based anytime planning with landmarks. Journal of Artificial Intelligence Research 39, 127–177 (2010)
Rintanen, J.: Heuristics for planning with SAT. In: Cohen, D. (ed.) CP 2010. LNCS, vol. 6308, pp. 414–428. Springer, Heidelberg (2010)
Rintanen, J., Heljanko, K., Niemelä, I.: Planning as satisfiability: parallel plans and algorithms for plan search. Artificial Intelligence 170(12-13), 1031–1080 (2006)
Ruml, W., Do, M.B., Zhou, R., Fromherz, M.P.J.: On-line planning and scheduling: An application to controlling modular printers. Journal of Artificial Intelligence Research 40, 415–468 (2011)
Simpson, R.M., Kitchin, D.E., McCluskey, T.L.: Planning domain definition using GIPO. Knowledge Engineering Review 22(2), 117–134 (2007)
Thayer, J., Ruml, W.: Bounded suboptimal search: A direct approach using inadmissible estimates. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (2011)
Vaquero, T.S., Romero, V., Tonidandel, F., Silva, J.R.: itSIMPLE 2.0: an integrated tool for designing planning domains. In: Proceedings of the 17th International Conference on Automated Planning and Scheduling (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hoffmann, J. (2011). Everything You Always Wanted to Know about Planning . In: Bach, J., Edelkamp, S. (eds) KI 2011: Advances in Artificial Intelligence. KI 2011. Lecture Notes in Computer Science(), vol 7006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24455-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-24455-1_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24454-4
Online ISBN: 978-3-642-24455-1
eBook Packages: Computer ScienceComputer Science (R0)