Abstract
The vast majority of work in planning to date has focused on state-independent action costs. However, if a planning task features state-dependent costs, using a cost model with state-independent costs means either introducing a modeling error, or potentially sacrificing compactness of the model. In this paper, we investigate the conflicting priorities of modeling accuracy and compactness empirically, with a particular focus on the extent of the negative impact of reduced modeling accuracy on (a) the quality of the resulting plans, and (b) the search guidance provided by heuristics that are fed with inaccurate cost models. Our empirical results show that the plan suboptimality introduced by ignoring state-dependent costs can range, depending on the domain, from inexistent to several orders of magnitude. Furthermore, our results show that the impact on heuristic guidance additionally depends strongly on the heuristic that is used, the specifics of how exactly the costs are represented, and whether one is interested in heuristic accuracy, node expansions, or overall runtime savings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bonet, B., Geffner, H.: Planning as heuristic search: new results. In: Biundo, S., Fox, M. (eds.) ECP 1999. LNCS (LNAI), vol. 1809, pp. 360–372. Springer, Heidelberg (2000). https://doi.org/10.1007/10720246_28
Ciardo, G., Siminiceanu, R.: Using edge-valued decision diagrams for symbolic generation of shortest paths. In: Proceedings of the Fourth International Conference on Formal Methods in Computer-Aided Design (FMCAD), pp. 256–273 (2002)
Fan, G., Müller, M., Holte, R.: Additive merge-and-shrink heuristics for diverse action costs. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), pp. 4287–4293 (2017)
Fan, G., Müller, M., Holte, R.: The two-edged nature of diverse action costs. In: Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling (ICAPS), pp. 98–106 (2017)
Geffner, H., Bonet, B.: A Concise Introduction to Models and Methods for Automated Planning. Morgan & Claypool Publishers, San Rafael (2013)
Geißer, F.: On planning with state-dependent action costs. Ph.D. dissertation, Albert-Ludwigs-Universität Freiburg (2018)
Geißer, F., Keller, T., Mattmüller, R.: Delete relaxations for planning with state-dependent action costs. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1573–1579 (2015)
Geißer, F., Keller, T., Mattmüller, R.: Abstractions for planning with state-dependent action costs. In: Proceedings of the 26th International Conference on Automated Planning and Scheduling (ICAPS), pp. 140–148 (2016)
Haslum, P., Botea, A., Helmert, M., Bonet, B., Koenig, S.: Domain-independent construction of pattern database heuristics for cost-optimal planning. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence, pp. 1007–1012 (2007)
Helmert, M.: The fast downward planning system. J. Artif. Intell. Res. 26, 191–246 (2006)
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 (ICAPS) (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 (ICAPS), pp. 176–183 (2007)
Helmert, M., Röger, G.: How good is almost perfect? In: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI 2008), pp. 944–949 (2008)
Ivankovic, F., Haslum, P., Gordon, D.: Planning with global state constraints and state-dependent action costs. In: Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS) (2019)
Keller, T., Pommerening, F., Seipp, J., Geißer, F., Mattmüller, R.: State-dependent cost partitionings for Cartesian abstractions in classical planning. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI), pp. 3161–3169 (2016)
Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley Longman, Boston (1984)
Speck, D., Geißer, F., Mattmüller, R.: Symbolic planning with edge-valued multi-valued decision diagrams. In: Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS), pp. 250–258 (2018)
Speck, D., Geißer, F., Mattmüller, R.: SYMPLE: symbolic planning based on EVMDDs. In: Ninth International Planning Competition (IPC-9): Planner Abstracts, pp. 91–94 (2018)
Acknowledgments
David Speck was supported by the German National Science Foundation (DFG) as part of the project EPSDAC (MA 7790/1-1). Florian Geißer was supported by ARC project DP180103446, “On-line planning for constrained autonomous agents in an uncertain world”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Corraya, S., Geißer, F., Speck, D., Mattmüller, R. (2019). An Empirical Study of the Usefulness of State-Dependent Action Costs in Planning. In: Benzmüller, C., Stuckenschmidt, H. (eds) KI 2019: Advances in Artificial Intelligence. KI 2019. Lecture Notes in Computer Science(), vol 11793. Springer, Cham. https://doi.org/10.1007/978-3-030-30179-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-30179-8_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30178-1
Online ISBN: 978-3-030-30179-8
eBook Packages: Computer ScienceComputer Science (R0)